code/14.movielens_recommendation-systems.ipynb

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   "source": [
    "***\n",
    "***\n",
    "# 使用Turicreate进行电影推荐\n",
    "***\n",
    "***\n"
   ]
  },
  {
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   "source": [
    "import turicreate as tc\n",
    "# set canvas to show sframes and sgraphs in ipython notebook\n",
    "# import matplotlib.pyplot as plt\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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     "slide_type": "slide"
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   "outputs": [],
   "source": [
    "# download data from: http://files.grouplens.org/datasets/movielens/ml-1m.zip"
   ]
  },
  {
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   "execution_count": 2,
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     "slide_type": "subslide"
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     "data": {
      "text/html": [
       "<pre>Finished parsing file /Users/datalab/bigdata/cjc/ml-1m/ratings.dat</pre>"
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      "text/plain": [
       "Finished parsing file /Users/datalab/bigdata/cjc/ml-1m/ratings.dat"
      ]
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       "Parsing completed. Parsed 100 lines in 0.281192 secs."
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     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[str]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
     ]
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      "text/html": [
       "<pre>Finished parsing file /Users/datalab/bigdata/cjc/ml-1m/ratings.dat</pre>"
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      "text/plain": [
       "Finished parsing file /Users/datalab/bigdata/cjc/ml-1m/ratings.dat"
      ]
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     "output_type": "display_data"
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   "source": [
    "data = tc.SFrame.read_csv('/Users/datalab/bigdata/cjc/ml-1m/ratings.dat', delimiter='\\n', \n",
    "                                header=False)['X1'].apply(lambda x: x.split('::')).unpack()\n",
    "for col in data.column_names():\n",
    "    data[col] = data[col].astype(int)\n",
    "data = data.rename({'X.0': 'user_id', 'X.1': 'movie_id', 'X.2': 'rating', 'X.3': 'timestamp'})\n",
    "#data.save('ratings')"
   ]
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     "data": {
      "text/html": [
       "<pre>Finished parsing file /Users/datalab/bigdata/cjc/ml-1m/users.dat</pre>"
      ],
      "text/plain": [
       "Finished parsing file /Users/datalab/bigdata/cjc/ml-1m/users.dat"
      ]
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       "<pre>Parsing completed. Parsed 100 lines in 0.028041 secs.</pre>"
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       "Parsing completed. Parsed 100 lines in 0.028041 secs."
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     "metadata": {},
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     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[str]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
     ]
    },
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     "data": {
      "text/html": [
       "<pre>Finished parsing file /Users/datalab/bigdata/cjc/ml-1m/users.dat</pre>"
      ],
      "text/plain": [
       "Finished parsing file /Users/datalab/bigdata/cjc/ml-1m/users.dat"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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       "<pre>Parsing completed. Parsed 6040 lines in 0.007235 secs.</pre>"
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       "Parsing completed. Parsed 6040 lines in 0.007235 secs."
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     "metadata": {},
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   "source": [
    "users = tc.SFrame.read_csv('/Users/datalab/bigdata/cjc/ml-1m/users.dat', delimiter='\\n', \n",
    "                                 header=False)['X1'].apply(lambda x: x.split('::')).unpack()\n",
    "users = users.rename({'X.0': 'user_id', 'X.1': 'gender', 'X.2': 'age', 'X.3': 'occupation', 'X.4': 'zip-code'})\n",
    "users['user_id'] = users['user_id'].astype(int)\n",
    "users.save('users')\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 28,
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     "end_time": "2019-06-14T16:22:48.875374Z",
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     "slide_type": "slide"
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   "outputs": [],
   "source": [
    "#items = tc.SFrame.read_csv('/Users/datalab/bigdata/ml-1m/movies.dat', delimiter='\\n', header=False)#['X1'].apply(lambda x: x.split('::')).unpack()\n",
    "# items = items.rename({'X.0': 'movie_id', 'X.1': 'title', 'X.2': 'genre'})\n",
    "# items['movie_id'] = items['movie_id'].astype(int)\n",
    "# items.save('items')"
   ]
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       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">user_id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">movie_id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">timestamp</th>\n",
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       "[1000209 rows x 4 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
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      "text/plain": [
       "Columns:\n",
       "\tuser_id\tint\n",
       "\tmovie_id\tint\n",
       "\trating\tint\n",
       "\ttimestamp\tint\n",
       "\n",
       "Rows: 1000209\n",
       "\n",
       "Data:\n",
       "+---------+----------+--------+-----------+\n",
       "| user_id | movie_id | rating | timestamp |\n",
       "+---------+----------+--------+-----------+\n",
       "|    1    |   1193   |   5    | 978300760 |\n",
       "|    1    |   661    |   3    | 978302109 |\n",
       "|    1    |   914    |   3    | 978301968 |\n",
       "|    1    |   3408   |   4    | 978300275 |\n",
       "|    1    |   2355   |   5    | 978824291 |\n",
       "|    1    |   1197   |   3    | 978302268 |\n",
       "|    1    |   1287   |   5    | 978302039 |\n",
       "|    1    |   2804   |   5    | 978300719 |\n",
       "|    1    |   594    |   4    | 978302268 |\n",
       "|    1    |   919    |   4    | 978301368 |\n",
       "+---------+----------+--------+-----------+\n",
       "[1000209 rows x 4 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
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    "#items"
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       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">F</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">35</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">95370</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[6040 rows x 5 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tuser_id\tint\n",
       "\tgender\tstr\n",
       "\tage\tstr\n",
       "\toccupation\tstr\n",
       "\tzip-code\tstr\n",
       "\n",
       "Rows: 6040\n",
       "\n",
       "Data:\n",
       "+---------+--------+-----+------------+----------+\n",
       "| user_id | gender | age | occupation | zip-code |\n",
       "+---------+--------+-----+------------+----------+\n",
       "|    1    |   F    |  1  |     10     |  48067   |\n",
       "|    2    |   M    |  56 |     16     |  70072   |\n",
       "|    3    |   M    |  25 |     15     |  55117   |\n",
       "|    4    |   M    |  45 |     7      |  02460   |\n",
       "|    5    |   M    |  25 |     20     |  55455   |\n",
       "|    6    |   F    |  50 |     9      |  55117   |\n",
       "|    7    |   M    |  35 |     1      |  06810   |\n",
       "|    8    |   M    |  25 |     12     |  11413   |\n",
       "|    9    |   M    |  25 |     17     |  61614   |\n",
       "|    10   |   F    |  35 |     1      |  95370   |\n",
       "+---------+--------+-----+------------+----------+\n",
       "[6040 rows x 5 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T06:33:14.874154Z",
     "start_time": "2018-05-05T06:33:13.913325Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "#data = data.join(items, on='movie_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-14T16:23:16.893160Z",
     "start_time": "2019-06-14T16:23:16.890595Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "#data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-15T07:03:02.788476Z",
     "start_time": "2019-06-15T07:03:02.784988Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "train_set, test_set = data.random_split(0.95, seed=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-15T07:03:33.095594Z",
     "start_time": "2019-06-15T07:03:12.909391Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 949852 observations with 6040 users and 3701 items.</pre>"
      ],
      "text/plain": [
       "    Data has 949852 observations with 6040 users and 3701 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.550091s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.550091s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training ranking_factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training ranking_factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 32       |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 32       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-09    |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-09    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | adagrad  |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | adagrad  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-09    |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-09    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| ranking_regularization         | Rank-based Regularization Weight                 | 0.25     |</pre>"
      ],
      "text/plain": [
       "| ranking_regularization         | Rank-based Regularization Weight                 | 0.25     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 25       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 25       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 118731 / 949852 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 118731 / 949852 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 16.6667           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 16.6667           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 4.16667           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 4.16667           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 1.04167           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 2       | 1.04167           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.260417          | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.260417          | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.0651042         | 1.8722                                   |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.0651042         | 1.8722                                   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.0325521         | 1.94425                                  |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.0325521         | 1.94425                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.016276          | 1.95877                                  |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.016276          | 1.95877                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 0.00813802        | 2.0441                                   |</pre>"
      ],
      "text/plain": [
       "| 7       | 0.00813802        | 2.0441                                   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.0651042         | 1.8722                                   |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.0651042         | 1.8722                                   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 110us        | 2.44718           | 1.1172                |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 110us        | 2.44718           | 1.1172                |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 536.251ms    | 2.09737           | 1.13925               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 1       | 536.251ms    | 2.09737           | 1.13925               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 1.05s        | 1.85594           | 1.06079               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 2       | 1.05s        | 1.85594           | 1.06079               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 1.55s        | 1.79883           | 1.03161               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 3       | 1.55s        | 1.79883           | 1.03161               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 2.06s        | 1.77231           | 1.02676               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 4       | 2.06s        | 1.77231           | 1.02676               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 2.57s        | 1.75455           | 1.02264               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 5       | 2.57s        | 1.75455           | 1.02264               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 5.81s        | 1.66968           | 0.995516              | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 10      | 5.81s        | 1.66968           | 0.995516              | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 20      | 12.34s       | 1.58039           | 0.969493              | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 20      | 12.34s       | 1.58039           | 0.969493              | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 25      | 15.69s       | 1.54869           | 0.961055              | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 25      | 15.69s       | 1.54869           | 0.961055              | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 1.57752</pre>"
      ],
      "text/plain": [
       "       Final objective value: 1.57752"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 0.95536</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 0.95536"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = tc.recommender.create(train_set, 'user_id', 'movie_id', 'rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-14T16:23:51.502615Z",
     "start_time": "2019-06-14T16:23:51.482326Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class                            : RankingFactorizationRecommender\n",
       "\n",
       "Schema\n",
       "------\n",
       "User ID                          : user_id\n",
       "Item ID                          : movie_id\n",
       "Target                           : rating\n",
       "Additional observation features  : 1\n",
       "User side features               : []\n",
       "Item side features               : []\n",
       "\n",
       "Statistics\n",
       "----------\n",
       "Number of observations           : 949852\n",
       "Number of users                  : 6040\n",
       "Number of items                  : 3701\n",
       "\n",
       "Training summary\n",
       "----------------\n",
       "Training time                    : 21.9973\n",
       "\n",
       "Model Parameters\n",
       "----------------\n",
       "Model class                      : RankingFactorizationRecommender\n",
       "num_factors                      : 32\n",
       "binary_target                    : 0\n",
       "side_data_factorization          : 1\n",
       "solver                           : auto\n",
       "nmf                              : 0\n",
       "max_iterations                   : 25\n",
       "\n",
       "Regularization Settings\n",
       "-----------------------\n",
       "regularization                   : 0.0\n",
       "regularization_type              : normal\n",
       "linear_regularization            : 0.0\n",
       "ranking_regularization           : 0.25\n",
       "unobserved_rating_value          : -1.7976931348623157e+308\n",
       "num_sampled_negative_examples    : 4\n",
       "ials_confidence_scaling_type     : auto\n",
       "ials_confidence_scaling_factor   : 1\n",
       "\n",
       "Optimization Settings\n",
       "---------------------\n",
       "init_random_sigma                : 0.01\n",
       "sgd_convergence_interval         : 4\n",
       "sgd_convergence_threshold        : 0.0\n",
       "sgd_max_trial_iterations         : 5\n",
       "sgd_sampling_block_size          : 131072\n",
       "sgd_step_adjustment_interval     : 4\n",
       "sgd_step_size                    : 0.0\n",
       "sgd_trial_sample_minimum_size    : 10000\n",
       "sgd_trial_sample_proportion      : 0.125\n",
       "step_size_decrease_rate          : 0.75\n",
       "additional_iterations_if_unhealthy : 5\n",
       "adagrad_momentum_weighting       : 0.9\n",
       "num_tempering_iterations         : 4\n",
       "tempering_regularization_start_value : 0.0\n",
       "track_exact_loss                 : 0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-15T07:04:21.017511Z",
     "start_time": "2019-06-15T07:04:17.764265Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Warning: Ignoring columns timestamp;</pre>"
      ],
      "text/plain": [
       "Warning: Ignoring columns timestamp;"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    To use these columns in scoring predictions, use a model that allows the use of additional features.</pre>"
      ],
      "text/plain": [
       "    To use these columns in scoring predictions, use a model that allows the use of additional features."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 949852 observations with 6040 users and 3701 items.</pre>"
      ],
      "text/plain": [
       "    Data has 949852 observations with 6040 users and 3701 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.426101s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.426101s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training model from provided data.</pre>"
      ],
      "text/plain": [
       "Training model from provided data."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Gathering per-item and per-user statistics.</pre>"
      ],
      "text/plain": [
       "Gathering per-item and per-user statistics."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+------------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Elapsed Time (Item Statistics) | % Complete |</pre>"
      ],
      "text/plain": [
       "| Elapsed Time (Item Statistics) | % Complete |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+------------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 27.234ms                       | 16.5       |</pre>"
      ],
      "text/plain": [
       "| 27.234ms                       | 16.5       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 42.954ms                       | 100        |</pre>"
      ],
      "text/plain": [
       "| 42.954ms                       | 100        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+------------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Setting up lookup tables.</pre>"
      ],
      "text/plain": [
       "Setting up lookup tables."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Processing data in one pass using dense lookup tables.</pre>"
      ],
      "text/plain": [
       "Processing data in one pass using dense lookup tables."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-------------------------------------+------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-------------------------------------+------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Elapsed Time (Constructing Lookups) | Total % Complete | Items Processed |</pre>"
      ],
      "text/plain": [
       "| Elapsed Time (Constructing Lookups) | Total % Complete | Items Processed |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-------------------------------------+------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-------------------------------------+------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 73.627ms                            | 0                | 2               |</pre>"
      ],
      "text/plain": [
       "| 73.627ms                            | 0                | 2               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2.79s                               | 100              | 3701            |</pre>"
      ],
      "text/plain": [
       "| 2.79s                               | 100              | 3701            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-------------------------------------+------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-------------------------------------+------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finalizing lookup tables.</pre>"
      ],
      "text/plain": [
       "Finalizing lookup tables."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Generating candidate set for working with new users.</pre>"
      ],
      "text/plain": [
       "Generating candidate set for working with new users."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished training in 2.82252s</pre>"
      ],
      "text/plain": [
       "Finished training in 2.82252s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m2 = tc.item_similarity_recommender.create(train_set, \n",
    "                                                 'user_id', 'movie_id', 'rating',\n",
    "                                 similarity_type='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-15T07:04:26.908070Z",
     "start_time": "2019-06-15T07:04:26.896928Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class                            : ItemSimilarityRecommender\n",
       "\n",
       "Schema\n",
       "------\n",
       "User ID                          : user_id\n",
       "Item ID                          : movie_id\n",
       "Target                           : rating\n",
       "Additional observation features  : 0\n",
       "User side features               : []\n",
       "Item side features               : []\n",
       "\n",
       "Statistics\n",
       "----------\n",
       "Number of observations           : 949852\n",
       "Number of users                  : 6040\n",
       "Number of items                  : 3701\n",
       "\n",
       "Training summary\n",
       "----------------\n",
       "Training time                    : 2.8226\n",
       "\n",
       "Model Parameters\n",
       "----------------\n",
       "Model class                      : ItemSimilarityRecommender\n",
       "threshold                        : 0.001\n",
       "similarity_type                  : pearson\n",
       "training_method                  : auto\n",
       "\n",
       "Other Settings\n",
       "--------------\n",
       "max_data_passes                  : 4096\n",
       "max_item_neighborhood_size       : 64\n",
       "nearest_neighbors_interaction_proportion_threshold : 0.05\n",
       "target_memory_usage              : 8589934592\n",
       "sparse_density_estimation_sample_size : 4096\n",
       "degree_approximation_threshold   : 4096\n",
       "seed_item_set_size               : 50"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-15T07:04:50.789017Z",
     "start_time": "2019-06-15T07:04:47.875428Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "compare_models: using 2811 users to estimate model performance\n",
      "PROGRESS: Evaluate model M0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 1000/2811 queries. users per second: 10084.7</pre>"
      ],
      "text/plain": [
       "recommendations finished on 1000/2811 queries. users per second: 10084.7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 2000/2811 queries. users per second: 10557.4</pre>"
      ],
      "text/plain": [
       "recommendations finished on 2000/2811 queries. users per second: 10557.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+----------------------+----------------------+\n",
      "| cutoff |     mean_recall      |    mean_precision    |\n",
      "+--------+----------------------+----------------------+\n",
      "|   1    | 0.004372314245294037 | 0.03344005691924596  |\n",
      "|   2    | 0.008439255238125647 | 0.030771967271433692 |\n",
      "|   3    | 0.011792773608123091 | 0.029764022293371297 |\n",
      "|   4    | 0.014103362205887681 | 0.027303450729277888 |\n",
      "|   5    | 0.017724646480050326 | 0.026894343649946677 |\n",
      "|   6    | 0.01985047799128097  | 0.02549507885687179  |\n",
      "|   7    | 0.023037645809147193 | 0.025054632311836182 |\n",
      "|   8    | 0.02564717744662357  | 0.024101743151903235 |\n",
      "|   9    | 0.027494985038662042 | 0.023123443614372085 |\n",
      "|   10   | 0.02954846065621093  | 0.022483102098897183 |\n",
      "+--------+----------------------+----------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "\n",
      "Overall RMSE: 0.988323739301448\n",
      "\n",
      "Per User RMSE (best)\n",
      "+---------+----------------------+-------+\n",
      "| user_id |         rmse         | count |\n",
      "+---------+----------------------+-------+\n",
      "|   4695  | 0.008856667044261357 |   1   |\n",
      "+---------+----------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per User RMSE (worst)\n",
      "+---------+-------------------+-------+\n",
      "| user_id |        rmse       | count |\n",
      "+---------+-------------------+-------+\n",
      "|   1102  | 2.957562522855876 |   1   |\n",
      "+---------+-------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (best)\n",
      "+----------+----------------------+-------+\n",
      "| movie_id |         rmse         | count |\n",
      "+----------+----------------------+-------+\n",
      "|   3674   | 0.012974611607248221 |   1   |\n",
      "+----------+----------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (worst)\n",
      "+----------+--------------------+-------+\n",
      "| movie_id |        rmse        | count |\n",
      "+----------+--------------------+-------+\n",
      "|   3886   | 3.4432479133103597 |   1   |\n",
      "+----------+--------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 1000/2811 queries. users per second: 23065.4</pre>"
      ],
      "text/plain": [
       "recommendations finished on 1000/2811 queries. users per second: 23065.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 2000/2811 queries. users per second: 24766.9</pre>"
      ],
      "text/plain": [
       "recommendations finished on 2000/2811 queries. users per second: 24766.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+-------------+----------------+\n",
      "| cutoff | mean_recall | mean_precision |\n",
      "+--------+-------------+----------------+\n",
      "|   1    |     0.0     |      0.0       |\n",
      "|   2    |     0.0     |      0.0       |\n",
      "|   3    |     0.0     |      0.0       |\n",
      "|   4    |     0.0     |      0.0       |\n",
      "|   5    |     0.0     |      0.0       |\n",
      "|   6    |     0.0     |      0.0       |\n",
      "|   7    |     0.0     |      0.0       |\n",
      "|   8    |     0.0     |      0.0       |\n",
      "|   9    |     0.0     |      0.0       |\n",
      "|   10   |     0.0     |      0.0       |\n",
      "+--------+-------------+----------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "\n",
      "Overall RMSE: 0.977554609754323\n",
      "\n",
      "Per User RMSE (best)\n",
      "+---------+-----------------------+-------+\n",
      "| user_id |          rmse         | count |\n",
      "+---------+-----------------------+-------+\n",
      "|   3872  | 4.440892098500626e-16 |   1   |\n",
      "+---------+-----------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per User RMSE (worst)\n",
      "+---------+--------------------+-------+\n",
      "| user_id |        rmse        | count |\n",
      "+---------+--------------------+-------+\n",
      "|   5214  | 3.2845314102161183 |   2   |\n",
      "+---------+--------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (best)\n",
      "+----------+------+-------+\n",
      "| movie_id | rmse | count |\n",
      "+----------+------+-------+\n",
      "|   1842   | 0.0  |   1   |\n",
      "+----------+------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (worst)\n",
      "+----------+------+-------+\n",
      "| movie_id | rmse | count |\n",
      "+----------+------+-------+\n",
      "|   572    | 4.0  |   1   |\n",
      "+----------+------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result = tc.recommender.util.compare_models(test_set, \n",
    "                                                  [m, m2],\n",
    "                                            user_sample=.5, skip_set=train_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Getting similar items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-14T16:24:53.589323Z",
     "start_time": "2019-06-14T16:24:53.568580Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">similar</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">score</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rank</th>\n",
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       "    </tr>\n",
       "</table>\n",
       "[10 rows x 4 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tmovie_id\tint\n",
       "\tsimilar\tint\n",
       "\tscore\tfloat\n",
       "\trank\tint\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+----------+---------+--------------------+------+\n",
       "| movie_id | similar |       score        | rank |\n",
       "+----------+---------+--------------------+------+\n",
       "|   1287   |   1262  | 0.8935538530349731 |  1   |\n",
       "|   1287   |   1272  | 0.8684239983558655 |  2   |\n",
       "|   1287   |   2662  | 0.8668187260627747 |  3   |\n",
       "|   1287   |   3366  | 0.8548122048377991 |  4   |\n",
       "|   1287   |   2948  | 0.8543752431869507 |  5   |\n",
       "|   1287   |   3062  | 0.8494184017181396 |  6   |\n",
       "|   1287   |   2947  | 0.8432653546333313 |  7   |\n",
       "|   1287   |   3836  | 0.8384832739830017 |  8   |\n",
       "|   1287   |   1304  | 0.8308332562446594 |  9   |\n",
       "|   1287   |   1250  | 0.8267531394958496 |  10  |\n",
       "+----------+---------+--------------------+------+\n",
       "[10 rows x 4 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.get_similar_items([1287])  # movie_id is Ben-Hur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T06:45:15.507691Z",
     "start_time": "2018-05-05T06:45:15.502035Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method get_similar_items in module graphlab.toolkits.recommender.util:\n",
      "\n",
      "get_similar_items(self, items=None, k=10, verbose=False) method of graphlab.toolkits.recommender.ranking_factorization_recommender.RankingFactorizationRecommender instance\n",
      "    Get the k most similar items for each item in items.\n",
      "    \n",
      "    Each type of recommender has its own model for the similarity\n",
      "    between items. For example, the item_similarity_recommender will\n",
      "    return the most similar items according to the user-chosen\n",
      "    similarity; the factorization_recommender will return the\n",
      "    nearest items based on the cosine similarity between latent item\n",
      "    factors.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    items : SArray or list; optional\n",
      "        An :class:`~graphlab.SArray` or list of item ids for which to get\n",
      "        similar items. If 'None', then return the `k` most similar items for\n",
      "        all items in the training set.\n",
      "    \n",
      "    k : int, optional\n",
      "        The number of similar items for each item.\n",
      "    \n",
      "    verbose : bool, optional\n",
      "        Progress printing is shown.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    out : SFrame\n",
      "        A SFrame with the top ranked similar items for each item. The\n",
      "        columns `item`, 'similar', 'score' and 'rank', where\n",
      "        `item` matches the item column name specified at training time.\n",
      "        The 'rank' is between 1 and `k` and 'score' gives the similarity\n",
      "        score of that item. The value of the score depends on the method\n",
      "        used for computing item similarities.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    \n",
      "    >>> sf = graphlab.SFrame({'user_id': [\"0\", \"0\", \"0\", \"1\", \"1\", \"2\", \"2\", \"2\"],\n",
      "                              'item_id': [\"a\", \"b\", \"c\", \"a\", \"b\", \"b\", \"c\", \"d\"]})\n",
      "    >>> m = graphlab.item_similarity_recommender.create(sf)\n",
      "    >>> nn = m.get_similar_items()\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(m.get_similar_items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "'score' gives the similarity score of that item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-14T16:25:06.875078Z",
     "start_time": "2019-06-14T16:25:06.872421Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# m.get_similar_items([1287]).join(items, on={'similar': 'movie_id'}).sort('rank')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Making recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-14T16:25:10.176791Z",
     "start_time": "2019-06-14T16:25:09.614271Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 1000/6040 queries. users per second: 11685.2</pre>"
      ],
      "text/plain": [
       "recommendations finished on 1000/6040 queries. users per second: 11685.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 2000/6040 queries. users per second: 11654.4</pre>"
      ],
      "text/plain": [
       "recommendations finished on 2000/6040 queries. users per second: 11654.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 3000/6040 queries. users per second: 11658.6</pre>"
      ],
      "text/plain": [
       "recommendations finished on 3000/6040 queries. users per second: 11658.6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 4000/6040 queries. users per second: 11321.5</pre>"
      ],
      "text/plain": [
       "recommendations finished on 4000/6040 queries. users per second: 11321.5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 5000/6040 queries. users per second: 11502.9</pre>"
      ],
      "text/plain": [
       "recommendations finished on 5000/6040 queries. users per second: 11502.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 6000/6040 queries. users per second: 11105.9</pre>"
      ],
      "text/plain": [
       "recommendations finished on 6000/6040 queries. users per second: 11105.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recs = m.recommend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-14T16:25:12.909100Z",
     "start_time": "2019-06-14T16:25:12.888333Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">user_id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">movie_id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">score</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.045622686663793</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "    </tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.766107517102884</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">858</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.747795152286218</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1196</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.689315832028316</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2858</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.678970253834652</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2396</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.5986619915758835</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">110</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.588308471063303</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2571</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.573408636072801</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[60400 rows x 4 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tuser_id\tint\n",
       "\tmovie_id\tint\n",
       "\tscore\tfloat\n",
       "\trank\tint\n",
       "\n",
       "Rows: 60400\n",
       "\n",
       "Data:\n",
       "+---------+----------+--------------------+------+\n",
       "| user_id | movie_id |       score        | rank |\n",
       "+---------+----------+--------------------+------+\n",
       "|    1    |   318    | 5.045622686663793  |  1   |\n",
       "|    1    |   1198   | 4.862424055854009  |  2   |\n",
       "|    1    |    50    |  4.76625474802606  |  3   |\n",
       "|    1    |   593    | 4.766107517102884  |  4   |\n",
       "|    1    |   858    | 4.747795152286218  |  5   |\n",
       "|    1    |   1196   | 4.689315832028316  |  6   |\n",
       "|    1    |   2858   | 4.678970253834652  |  7   |\n",
       "|    1    |   2396   | 4.5986619915758835 |  8   |\n",
       "|    1    |   110    | 4.588308471063303  |  9   |\n",
       "|    1    |   2571   | 4.573408636072801  |  10  |\n",
       "+---------+----------+--------------------+------+\n",
       "[60400 rows x 4 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-14T16:25:17.489601Z",
     "start_time": "2019-06-14T16:25:17.232492Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">user_id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">movie_id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3468</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">978294008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">978293924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2951</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">978294282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1214</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">978294260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1036</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">978294282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">260</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">978294199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2028</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">978294230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">480</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">978294008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1196</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">978294199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1198</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">978294199</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[? rows x 4 columns]<br/>Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.<br/>You can use sf.materialize() to force materialization.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tuser_id\tint\n",
       "\tmovie_id\tint\n",
       "\trating\tint\n",
       "\ttimestamp\tint\n",
       "\n",
       "Rows: Unknown\n",
       "\n",
       "Data:\n",
       "+---------+----------+--------+-----------+\n",
       "| user_id | movie_id | rating | timestamp |\n",
       "+---------+----------+--------+-----------+\n",
       "|    4    |   3468   |   5    | 978294008 |\n",
       "|    4    |   1210   |   3    | 978293924 |\n",
       "|    4    |   2951   |   4    | 978294282 |\n",
       "|    4    |   1214   |   4    | 978294260 |\n",
       "|    4    |   1036   |   4    | 978294282 |\n",
       "|    4    |   260    |   5    | 978294199 |\n",
       "|    4    |   2028   |   5    | 978294230 |\n",
       "|    4    |   480    |   4    | 978294008 |\n",
       "|    4    |   1196   |   2    | 978294199 |\n",
       "|    4    |   1198   |   5    | 978294199 |\n",
       "+---------+----------+--------+-----------+\n",
       "[? rows x 4 columns]\n",
       "Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n",
       "You can use sf.materialize() to force materialization."
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['user_id'] == 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-14T16:25:22.784880Z",
     "start_time": "2019-06-14T16:25:22.782529Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# m.recommend(users=[4], k=20).join(items, on='movie_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Recommendations for new users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-14T16:25:34.317252Z",
     "start_time": "2019-06-14T16:25:34.296992Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">movie_id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">user_id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">30</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1000</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">900</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">883</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">251</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">200</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">199</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">180</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">120</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">991</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[11 rows x 3 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tmovie_id\tint\n",
       "\tuser_id\tint\n",
       "\trating\tint\n",
       "\n",
       "Rows: 11\n",
       "\n",
       "Data:\n",
       "+----------+---------+--------+\n",
       "| movie_id | user_id | rating |\n",
       "+----------+---------+--------+\n",
       "|    30    |  99999  |   2    |\n",
       "|   1000   |  99999  |   1    |\n",
       "|   900    |  99999  |   3    |\n",
       "|   883    |  99999  |   4    |\n",
       "|   251    |  99999  |   0    |\n",
       "|   200    |  99999  |   0    |\n",
       "|   199    |  99999  |   1    |\n",
       "|   180    |  99999  |   1    |\n",
       "|   120    |  99999  |   1    |\n",
       "|   991    |  99999  |   2    |\n",
       "+----------+---------+--------+\n",
       "[11 rows x 3 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_data = tc.SFrame()\n",
    "recent_data['movie_id'] = [30, 1000, 900, 883, 251, 200, 199, 180, 120, 991, 1212] \n",
    "recent_data['user_id'] = 99999\n",
    "recent_data['rating'] = [2, 1, 3, 4, 0, 0, 1, 1, 1, 2, 3]\n",
    "recent_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-14T16:25:45.789843Z",
     "start_time": "2019-06-14T16:25:45.767211Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">user_id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">movie_id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">score</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3881</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3607</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1830</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">989</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3172</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3233</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">787</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3382</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3656</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">99999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3280</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 4 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tuser_id\tint\n",
       "\tmovie_id\tint\n",
       "\tscore\tfloat\n",
       "\trank\tint\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+---------+----------+-------+------+\n",
       "| user_id | movie_id | score | rank |\n",
       "+---------+----------+-------+------+\n",
       "|  99999  |   3881   |  5.0  |  1   |\n",
       "|  99999  |   3607   |  5.0  |  2   |\n",
       "|  99999  |   1830   |  5.0  |  3   |\n",
       "|  99999  |   989    |  5.0  |  4   |\n",
       "|  99999  |   3172   |  5.0  |  5   |\n",
       "|  99999  |   3233   |  5.0  |  6   |\n",
       "|  99999  |   787    |  5.0  |  7   |\n",
       "|  99999  |   3382   |  5.0  |  8   |\n",
       "|  99999  |   3656   |  5.0  |  9   |\n",
       "|  99999  |   3280   |  5.0  |  10  |\n",
       "+---------+----------+-------+------+\n",
       "[10 rows x 4 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.recommend(users=[99999], new_observation_data=recent_data)#.join(items, on='movie_id').sort('rank')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Saving and loading models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "m.save('my_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "m_again = graphlab.load_model('my_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "m_again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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