(KDT)_아이템_기반_협업_필터링_추천.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "(KDT) 아이템 기반 협업 필터링 추천",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/learn-programmers/programmers_kdt_II/blob/main/(KDT)_%EC%95%84%EC%9D%B4%ED%85%9C_%EA%B8%B0%EB%B0%98_%ED%98%91%EC%97%85_%ED%95%84%ED%84%B0%EB%A7%81_%EC%B6%94%EC%B2%9C.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "roEuxynyeM0l"
      },
      "source": [
        "surprise 모듈을 설치"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Ep7L0e2ldgY",
        "outputId": "3b914c14-7875-43e6-9a46-73709ecac97e"
      },
      "source": [
        "!pip install surprise"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: surprise in /usr/local/lib/python3.7/dist-packages (0.1)\n",
            "Requirement already satisfied: scikit-surprise in /usr/local/lib/python3.7/dist-packages (from surprise) (1.1.1)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from scikit-surprise->surprise) (1.15.0)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-surprise->surprise) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.11.2 in /usr/local/lib/python3.7/dist-packages (from scikit-surprise->surprise) (1.19.5)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-surprise->surprise) (1.0.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AUirw_6Uzb6o",
        "outputId": "2860fb8c-e915-4052-d17c-3f8e585b5c88"
      },
      "source": [
        "!wget \"https://grepp-reco-test.s3.ap-northeast-2.amazonaws.com/movielens/movies.csv\"\n",
        "!wget \"https://grepp-reco-test.s3.ap-northeast-2.amazonaws.com/movielens/ratings.csv\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-02-26 07:37:01--  https://grepp-reco-test.s3.ap-northeast-2.amazonaws.com/movielens/movies.csv\n",
            "Resolving grepp-reco-test.s3.ap-northeast-2.amazonaws.com (grepp-reco-test.s3.ap-northeast-2.amazonaws.com)... 52.219.58.91\n",
            "Connecting to grepp-reco-test.s3.ap-northeast-2.amazonaws.com (grepp-reco-test.s3.ap-northeast-2.amazonaws.com)|52.219.58.91|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 458390 (448K) [text/csv]\n",
            "Saving to: ‘movies.csv.2’\n",
            "\n",
            "movies.csv.2        100%[===================>] 447.65K   369KB/s    in 1.2s    \n",
            "\n",
            "2021-02-26 07:37:03 (369 KB/s) - ‘movies.csv.2’ saved [458390/458390]\n",
            "\n",
            "--2021-02-26 07:37:03--  https://grepp-reco-test.s3.ap-northeast-2.amazonaws.com/movielens/ratings.csv\n",
            "Resolving grepp-reco-test.s3.ap-northeast-2.amazonaws.com (grepp-reco-test.s3.ap-northeast-2.amazonaws.com)... 52.219.60.55\n",
            "Connecting to grepp-reco-test.s3.ap-northeast-2.amazonaws.com (grepp-reco-test.s3.ap-northeast-2.amazonaws.com)|52.219.60.55|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2438266 (2.3M) [text/csv]\n",
            "Saving to: ‘ratings.csv.2’\n",
            "\n",
            "ratings.csv.2       100%[===================>]   2.33M  1.29MB/s    in 1.8s    \n",
            "\n",
            "2021-02-26 07:37:06 (1.29 MB/s) - ‘ratings.csv.2’ saved [2438266/2438266]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oXJRj_2MF4qX",
        "outputId": "de2d6f6c-586c-4851-dd5e-100b76edd21d"
      },
      "source": [
        "!ls -tl"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 8500\n",
            "drwxr-xr-x 1 root root    4096 Feb 24 17:49 sample_data\n",
            "-rw-r--r-- 1 root root  458390 Feb 23 02:04 movies.csv\n",
            "-rw-r--r-- 1 root root  458390 Feb 23 02:04 movies.csv.1\n",
            "-rw-r--r-- 1 root root  458390 Feb 23 02:04 movies.csv.2\n",
            "-rw-r--r-- 1 root root 2438266 Feb 23 02:04 ratings.csv\n",
            "-rw-r--r-- 1 root root 2438266 Feb 23 02:04 ratings.csv.1\n",
            "-rw-r--r-- 1 root root 2438266 Feb 23 02:04 ratings.csv.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZQfxxifzG6k"
      },
      "source": [
        "# 데이터 로딩\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4jdjZN254pO"
      },
      "source": [
        "from surprise import Dataset\n",
        "from surprise import Reader\n",
        "\n",
        "from collections import defaultdict\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9m_UFiBnx6m"
      },
      "source": [
        "movies = pd.read_csv(\"movies.csv\")\n",
        "ratings = pd.read_csv(\"ratings.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "kym07ydsoou8",
        "outputId": "fd0bc7cd-e7b7-43e1-f8a0-e24e138adb93"
      },
      "source": [
        "movies.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId  ...                                       genres\n",
              "0        1  ...  Adventure|Animation|Children|Comedy|Fantasy\n",
              "1        2  ...                   Adventure|Children|Fantasy\n",
              "2        3  ...                               Comedy|Romance\n",
              "3        4  ...                         Comedy|Drama|Romance\n",
              "4        5  ...                                       Comedy\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "K82Avgs3pHAe",
        "outputId": "578b5a75-8cc3-4981-e176-4c3447d59fae"
      },
      "source": [
        "ratings.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>31</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1260759144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1029</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1260759179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1061</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1260759182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1129</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1260759185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1172</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1260759205</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userId  movieId  rating   timestamp\n",
              "0       1       31     2.5  1260759144\n",
              "1       1     1029     3.0  1260759179\n",
              "2       1     1061     3.0  1260759182\n",
              "3       1     1129     2.0  1260759185\n",
              "4       1     1172     4.0  1260759205"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQqs1GQIpI2y"
      },
      "source": [
        "movie_ratings = pd.merge(movies, ratings, left_on='movieId', right_on='movieId')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "iXrDNF1UpWgF",
        "outputId": "dbd37342-fc1e-4d68-8bb5-f92aa914182d"
      },
      "source": [
        "movie_ratings.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>userId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>851866703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>9</td>\n",
              "      <td>4.0</td>\n",
              "      <td>938629179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>13</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1331380058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>15</td>\n",
              "      <td>2.0</td>\n",
              "      <td>997938310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>19</td>\n",
              "      <td>3.0</td>\n",
              "      <td>855190091</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId             title  ... rating   timestamp\n",
              "0        1  Toy Story (1995)  ...    3.0   851866703\n",
              "1        1  Toy Story (1995)  ...    4.0   938629179\n",
              "2        1  Toy Story (1995)  ...    5.0  1331380058\n",
              "3        1  Toy Story (1995)  ...    2.0   997938310\n",
              "4        1  Toy Story (1995)  ...    3.0   855190091\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udf_2XkOpZ2L",
        "outputId": "ffbf538e-1223-421c-90f3-a17ca8058d47"
      },
      "source": [
        "movies.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9125, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90izVbp0pcO1",
        "outputId": "438f090d-c181-407a-af71-139342d47483"
      },
      "source": [
        "ratings.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100004, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l8j8KQfwpeL4",
        "outputId": "5b65c658-bf42-4184-9a64-258b76ac5c07"
      },
      "source": [
        "movie_ratings.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100004, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ywKCC6ZqXl33"
      },
      "source": [
        "def getMovieName(movie_ratings, movieID):\n",
        "    return movie_ratings[movie_ratings[\"movieId\"] == movieID][[\"title\", \"genres\"]].values[0]\n",
        "\n",
        "def getMovieID(movie_ratings, movieName):\n",
        "    return movie_ratings[movie_ratings[\"title\"] == movieName][[\"movieId\", \"genres\"]].values[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHeH7DsyyxCa"
      },
      "source": [
        "## 영화 데이터를 surprise 모듈을 통해 로딩"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgtyCtMaw8Eq"
      },
      "source": [
        "reader = Reader(line_format='user item rating timestamp', sep=',', skip_lines=1)\n",
        "data = Dataset.load_from_file(\"ratings.csv\", reader=reader)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xn5gBOTgzWAR"
      },
      "source": [
        "from surprise import KNNBasic\n",
        "import heapq\n",
        "from collections import defaultdict"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpkLzRulzza3"
      },
      "source": [
        "# 데이터를 훈련용과 테스트용으로 나누지 말고 모두 리턴\n",
        "trainSet = data.build_full_trainset()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7SulaH4Qz42-",
        "outputId": "7ef0e487-f094-4ce8-ffb9-1dfc71ec4134"
      },
      "source": [
        "sim_options = {\n",
        "    'name': 'cosine',\n",
        "    'user_based': False\n",
        "}\n",
        "\n",
        "# 아이템 기반과 코사인 유사도 기반으로 동작하는 모델 생성\n",
        "model = KNNBasic(sim_options=sim_options)\n",
        "# 데이터로부터 아이템 벡터 생성\n",
        "model.fit(trainSet)\n",
        "# 아이템간의 유사도 행렬 계산\n",
        "simsMatrix = model.compute_similarities()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Computing the cosine similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "Computing the cosine similarity matrix...\n",
            "Done computing similarity matrix.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dYnKIcfx0BNh"
      },
      "source": [
        "testUserId = '85'\n",
        "k = 10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "6ZyzNRA5Yw7o",
        "outputId": "e12258fd-2b65-4c81-c747-0b7de0f0a392"
      },
      "source": [
        "pd.set_option('display.max_colwidth', -1)\n",
        "display(movie_ratings[movie_ratings[\"userId\"] == int(testUserId)].sort_values(\"rating\", ascending=False))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>userId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>263</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "      <td>85</td>\n",
              "      <td>5.0</td>\n",
              "      <td>837511784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7443</th>\n",
              "      <td>255</td>\n",
              "      <td>Jerky Boys, The (1995)</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>85</td>\n",
              "      <td>5.0</td>\n",
              "      <td>837512635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19363</th>\n",
              "      <td>648</td>\n",
              "      <td>Mission: Impossible (1996)</td>\n",
              "      <td>Action|Adventure|Mystery|Thriller</td>\n",
              "      <td>85</td>\n",
              "      <td>5.0</td>\n",
              "      <td>837511448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17405</th>\n",
              "      <td>589</td>\n",
              "      <td>Terminator 2: Judgment Day (1991)</td>\n",
              "      <td>Action|Sci-Fi</td>\n",
              "      <td>85</td>\n",
              "      <td>5.0</td>\n",
              "      <td>837507044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14386</th>\n",
              "      <td>480</td>\n",
              "      <td>Jurassic Park (1993)</td>\n",
              "      <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
              "      <td>85</td>\n",
              "      <td>5.0</td>\n",
              "      <td>837511743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16429</th>\n",
              "      <td>546</td>\n",
              "      <td>Super Mario Bros. (1993)</td>\n",
              "      <td>Action|Adventure|Children|Comedy|Fantasy|Sci-Fi</td>\n",
              "      <td>85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>837512482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11769</th>\n",
              "      <td>366</td>\n",
              "      <td>Wes Craven's New Nightmare (Nightmare on Elm Street Part 7: Freddy's Finale, A) (1994)</td>\n",
              "      <td>Drama|Horror|Mystery|Thriller</td>\n",
              "      <td>85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>837512580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12156</th>\n",
              "      <td>374</td>\n",
              "      <td>Richie Rich (1994)</td>\n",
              "      <td>Children|Comedy</td>\n",
              "      <td>85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>837512723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8420</th>\n",
              "      <td>291</td>\n",
              "      <td>Poison Ivy II (1996)</td>\n",
              "      <td>Drama|Thriller</td>\n",
              "      <td>85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>837512611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8237</th>\n",
              "      <td>282</td>\n",
              "      <td>Nell (1994)</td>\n",
              "      <td>Drama</td>\n",
              "      <td>85</td>\n",
              "      <td>1.0</td>\n",
              "      <td>837512962</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>107 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       movieId  ...  timestamp\n",
              "263    2        ...  837511784\n",
              "7443   255      ...  837512635\n",
              "19363  648      ...  837511448\n",
              "17405  589      ...  837507044\n",
              "14386  480      ...  837511743\n",
              "...    ...      ...        ...\n",
              "16429  546      ...  837512482\n",
              "11769  366      ...  837512580\n",
              "12156  374      ...  837512723\n",
              "8420   291      ...  837512611\n",
              "8237   282      ...  837512962\n",
              "\n",
              "[107 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Xm-xEjrYujc"
      },
      "source": [
        "testUserInnerID = trainSet.to_inner_uid(testUserId)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXT62k1C0MXA"
      },
      "source": [
        "# 이 사용자가 좋아한 영화 k개를 읽어서 kNeighbors에 저장\n",
        "testUserRatings = trainSet.ur[testUserInnerID]\n",
        "kNeighbors = heapq.nlargest(k, testUserRatings, key=lambda t: t[1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vFfjdVUM0T0p"
      },
      "source": [
        "# 이 사용자 좋아한 영화들과 비슷한 영화를 찾아서 candidates에 유사도 가중치를 곱해서 저장\n",
        "candidates = defaultdict(float)\n",
        "for itemID, rating in kNeighbors:\n",
        "    similarityRow = simsMatrix[itemID]\n",
        "    for innerID, score in enumerate(similarityRow):\n",
        "        candidates[innerID] += score * (rating)        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LB0Z8koI0WWR"
      },
      "source": [
        "# 사용자가 이미 본 아이템들을 기록\n",
        "watched = {}\n",
        "for itemID, rating in trainSet.ur[testUserInnerID]:\n",
        "    watched[itemID] = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lj-Tmcat0YlM",
        "outputId": "88d6b23e-2ee9-4ad8-8e94-a05db62b73c0"
      },
      "source": [
        "pos = 0\n",
        "for itemID, ratingSum in sorted(candidates.items(), key=lambda t: t[1], reverse=True):\n",
        "    if not itemID in watched:\n",
        "        movieID = trainSet.to_raw_iid(itemID)\n",
        "        print(movieID, getMovieName(movie_ratings, int(movieID)), ratingSum)\n",
        "        pos += 1\n",
        "        if (pos > 10):\n",
        "            break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3136 ['James Dean Story, The (1957)' 'Documentary'] 50.0\n",
            "2607 ['Get Real (1998)' 'Drama|Romance'] 49.93620560356324\n",
            "259 ['Kiss of Death (1995)' 'Crime|Drama|Thriller'] 49.83440938875971\n",
            "998 ['Set It Off (1996)' 'Action|Crime'] 49.818661078285594\n",
            "1935 ['How Green Was My Valley (1941)' 'Drama|Musical|Romance'] 49.71992040532635\n",
            "1440 ['Amos & Andrew (1993)' 'Comedy'] 49.69868472501265\n",
            "269 ['My Crazy Life (Mi vida loca) (1993)' 'Drama'] 49.691452437730206\n",
            "988 ['Grace of My Heart (1996)' 'Comedy|Drama'] 49.631279483226095\n",
            "2068 ['Fanny and Alexander (Fanny och Alexander) (1982)'\n",
            " 'Drama|Fantasy|Mystery'] 49.628498357279526\n",
            "896 ['Wild Reeds (Les roseaux sauvages) (1994)' 'Drama'] 49.58113202209386\n",
            "2626 ['Edge of Seventeen (1998)' 'Comedy|Drama|Romance'] 49.565143823458385\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKAMNwFq8w8t"
      },
      "source": [
        "def recommendForItem(userID):\n",
        "    testUserInnerID = trainSet.to_inner_uid(userID)\n",
        "    testUserRatings = trainSet.ur[testUserInnerID]\n",
        "\n",
        "    kNeighbors = heapq.nlargest(k, testUserRatings, key=lambda t: t[1])\n",
        "\n",
        "    candidates = defaultdict(float)\n",
        "    for itemID, rating in kNeighbors:\n",
        "        similarityRow = simsMatrix[itemID]\n",
        "        for innerID, score in enumerate(similarityRow):\n",
        "            candidates[innerID] += score * (rating) \n",
        " \n",
        "    watched = {}\n",
        "    for itemID, rating in trainSet.ur[testUserInnerID]:\n",
        "        watched[itemID] = 1\n",
        "\n",
        "    pos = 0\n",
        "    for itemID, ratingSum in sorted(candidates.items(), key=itemgetter(1), reverse=True):\n",
        "        if not itemID in watched:\n",
        "            movieID = trainSet.to_raw_iid(itemID)\n",
        "            print(movieID, getMovieName(movie_ratings, int(movieID)), ratingSum)\n",
        "            pos += 1\n",
        "            if (pos > 10):\n",
        "                break      "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyjOALwuYit2"
      },
      "source": [
        "recommendForItem('85')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wxfvVA5tYkeB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}