recommender-systems/04_implicit_feedback.ipynb

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        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/PreferredAI/tutorials/blob/master/recommender-systems/04_implicit_feedback.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/PreferredAI/tutorials/blob/master/recommender-systems/04_implicit_feedback.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzPrmZOMtXt1"
      },
      "source": [
        "# Implicit Feedback\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cBBggZxMmFG3"
      },
      "source": [
        "There are two types of feedback on which most recommender systems would be based. Explicit feedback deals with clear and quantified expression of preferences by a user, in terms of both likes and dislikes. An excellent example would be ratings. However, much more common is implicit feedback, where users provide some positive signals, but the contrast to negative signals may be absent or at least much less evident. An example would be clicks on links or views when browsing.  In this tutorial, we will focus on implicit feedback."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MqktDa7H2hKz"
      },
      "source": [
        "## 1. Setup"
      ]
    },
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          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.5/18.5 MB\u001b[0m \u001b[31m52.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install --quiet cornac==1.15.4 adjustText"
      ]
    },
    {
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      "execution_count": 2,
      "metadata": {
        "colab": {
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        "outputId": "e256a590-62f6-4261-bcc8-ff65c72a314e"
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          "text": [
            "System version: 3.10.11 (main, Apr  5 2023, 14:15:10) [GCC 9.4.0]\n",
            "Cornac version: 1.15.4\n",
            "Tensorflow version: 2.12.0\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import sys\n",
        "from collections import defaultdict\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from adjustText import adjust_text\n",
        "%matplotlib inline\n",
        "\n",
        "import cornac\n",
        "from cornac.utils import cache\n",
        "from cornac.datasets import movielens\n",
        "from cornac.eval_methods import RatioSplit\n",
        "from cornac.models import MF, BPR, WMF\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "print(f\"System version: {sys.version}\")\n",
        "print(f\"Cornac version: {cornac.__version__}\")\n",
        "print(f\"Tensorflow version: {tf.__version__}\")\n",
        "\n",
        "SEED = 42\n",
        "VERBOSE = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r76coF3ZyWxb"
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      "source": [
        "## 2. Weighted Matrix Factorization (WMF)\n",
        "\n",
        "We use the term **adoption matrix** to refer to interactions between users and items.  In the case of explicit feedback, it is the rating matrix, while in the case of implicit feedback, it can be a matrix of click counts or binary.\n",
        "\n",
        "Weighted matrix factorization predicts an adoption of user $i$ to item $j$ using inner product of user and item factors as $\\hat{p}_{i,j} = \\mathbf{u}_i^T \\mathbf{v}_j$.  The difference between Weighted MF and Original MF is that WMF tries to predict *unobserved* adoptions while MF ignores them during training. The convention by Cornac library is to derive observed and unobserved adoptions from the rating matrix as follows.\n",
        "\n",
        "\\begin{equation}\n",
        "p_{i,j} = \n",
        "\\begin{cases} \n",
        "r_{i, j} &\\mbox{if } r_{i,j} > 0 \\\\\n",
        "0 & \\mbox{otherwise} \n",
        "\\end{cases}\n",
        "\\end{equation}\n",
        "\n",
        "The important principle is that WMF usually assigns a higher confidence weight when fitting *observed* adoptions than when fitting *unobserved* adoptions. There are several ways to introduce the confidence scores.  Here we use the convention by Cornac library, which implements the following rules:\n",
        "\n",
        "\\begin{equation}\n",
        "c_{i,j} = \n",
        "\\begin{cases} \n",
        "a &\\mbox{if } r_{i,j} > 0 \\\\\n",
        "b & \\mbox{otherwise }\n",
        "\\end{cases}\n",
        "\\end{equation}\n",
        "\n",
        "Concretely, WMF minimizes the following loss function:\n",
        "\n",
        "$$ \\mathcal{L}(\\mathbf{U,V}|\\lambda) = \\frac{1}{2} \\sum_{i,j} c_{i,j} (p_{i,j} - \\mathbf{u}_i^T \\mathbf{v}_j)^2 + \\frac{\\lambda}{2} \\sum_{i=1}^{N} ||\\mathbf{u}_i||^2 + \\frac{\\lambda}{2} \\sum_{j=1}^{M} ||\\mathbf{v}_j||^2 $$\n",
        "\n",
        "Let's now compare how WMF and MF are different."
      ]
    },
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      "outputs": [
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          "name": "stdout",
          "text": [
            "Data from http://files.grouplens.org/datasets/movielens/ml-100k/u.data\n",
            "will be cached into /root/.cornac/ml-100k/u.data\n"
          ]
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          "text": [
            "File cached!\n",
            "\n",
            "TEST:\n",
            "...\n",
            "          |   RMSE |    AUC |  F1@10 |    MAP |    MRR | NDCG@10 | NDCG@20 | NDCG@30 | Precision@10 | Recall@10 | Train (s) | Test (s)\n",
            "--------- + ------ + ------ + ------ + ------ + ------ + ------- + ------- + ------- + ------------ + --------- + --------- + --------\n",
            "WMF(K=50) | 1.1012 | 0.8980 | 0.0734 | 0.0956 | 0.2387 |  0.1068 |  0.1292 |  0.1506 |       0.0968 |    0.0800 |   14.0206 |   6.3769\n",
            "MF(K=50)  | 0.8909 | 0.6573 | 0.0290 | 0.0402 | 0.1453 |  0.0539 |  0.0565 |  0.0626 |       0.0524 |    0.0241 |    0.2283 |   3.5900\n",
            "\n"
          ]
        }
      ],
      "source": [
        "K = 50\n",
        "wmf = WMF(k=K, max_iter=100, a=1.0, b=0.01, learning_rate=0.001, lambda_u=0.01, lambda_v=0.01,\n",
        "          verbose=VERBOSE, seed=SEED, name=f\"WMF(K={K})\")\n",
        "mf = MF(k=K, max_iter=20, learning_rate=0.01, lambda_reg=0.01, \n",
        "        verbose=VERBOSE, seed=SEED, name=f\"MF(K={K})\")\n",
        "\n",
        "eval_metrics = [\n",
        "  cornac.metrics.RMSE(), \n",
        "  cornac.metrics.AUC(),\n",
        "  cornac.metrics.Precision(k=10),\n",
        "  cornac.metrics.Recall(k=10),\n",
        "  cornac.metrics.FMeasure(k=10),\n",
        "  cornac.metrics.NDCG(k=[10, 20, 30]),\n",
        "  cornac.metrics.MRR(),\n",
        "  cornac.metrics.MAP()\n",
        "]\n",
        "\n",
        "data = movielens.load_feedback(variant=\"100K\")\n",
        "rs = RatioSplit(data, test_size=0.2, seed=SEED, verbose=VERBOSE)\n",
        "\n",
        "cornac.Experiment(eval_method=rs, models=[wmf, mf], metrics=eval_metrics).run()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bWO_aF1iBwI5"
      },
      "source": [
        "As we can observe, the strength of the MF model is the ability to predict ratings well (lower RMSE). However, WMF model is designed to rank items, by fitting binary adoptions, thus it outperforms MF across all the listed ranking metrics. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8iXcfuKsVa8"
      },
      "source": [
        "## 3. Bayesian Personalized Ranking (BPR)\n",
        "\n",
        "Implicit feedback can also be turned into another form of expression, namely ordinal preferences.  Given an *observed* feedback $(i, j)$ between user $i$ and item $j$, another item $l$ (with *no observed interaction* with user $i$) is coupled with the pair to form a triple $(i, j, l)$.  Thus, there are now notions of *positive* and *negative* items.  The training data consists of ordinal triples $D_S = \\{(i, j, l) | r_{ij} \\in \\mathbf{R}^+ \\land r_{il} \\in \\mathbf{R}^-\\}$.\n",
        "\n",
        "BPR maximizes the joint probability over all triples:\n",
        "\n",
        "$$ \\prod_{(i,j,l) \\in D_S} p(j >_{i} l) = \\prod_{(i,j,l) \\in D_S} \\sigma(\\mathbf{u}_i^T \\mathbf{v}_j - \\mathbf{u}_i^T \\mathbf{v}_l) $$\n",
        "\n",
        "where $\\sigma(*)$ is the logistic *sigmoid* function and the preference scoring function is the inner product of user and item latent factors, inspired by the matrix factorization method.\n",
        "\n",
        "By minimizing *negative log-likelihood* with *l2-regularization*, we arrive at the following loss function:\n",
        "\n",
        "$$ \\mathcal{L}(\\mathbf{U,V}|\\lambda) = \\sum_{i,j,l} \\ln (1 + \\exp\\{-(\\mathbf{u}_i^T \\mathbf{v}_j - \\mathbf{u}_i^T \\mathbf{v}_l)\\}) + \\frac{\\lambda}{2} \\sum_{i=1}^{N} ||\\mathbf{u}_i||^2 + \\frac{\\lambda}{2} \\sum_{j=1}^{M} ||\\mathbf{v}_j||^2 $$\n",
        "\n",
        "Let's compare the two models BPR and WMF."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "NZ2Al6omrlEP",
        "outputId": "8dc8c226-da19-4b2f-ad5b-e44277d1a454"
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      "outputs": [
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          "text": [
            "\n",
            "TEST:\n",
            "...\n",
            "          |   RMSE |    AUC |  F1@10 |    MAP |    MRR | NDCG@10 | NDCG@20 | NDCG@30 | Precision@10 | Recall@10 | Train (s) | Test (s)\n",
            "--------- + ------ + ------ + ------ + ------ + ------ + ------- + ------- + ------- + ------------ + --------- + --------- + --------\n",
            "BPR(K=50) | 1.5631 | 0.9356 | 0.0859 | 0.1146 | 0.2337 |  0.1096 |  0.1393 |  0.1662 |       0.0938 |    0.1000 |    3.8848 |   2.8085\n",
            "WMF(K=50) | 1.1012 | 0.8980 | 0.0734 | 0.0956 | 0.2387 |  0.1068 |  0.1292 |  0.1506 |       0.0968 |    0.0800 |    4.1856 |   2.3766\n",
            "\n"
          ]
        }
      ],
      "source": [
        "K = 50\n",
        "bpr = BPR(k=K, max_iter=200, learning_rate=0.01, lambda_reg=0.001, \n",
        "          verbose=VERBOSE, seed=SEED, name=f\"BPR(K={K})\")\n",
        "wmf = WMF(k=K, max_iter=100, a=1.0, b=0.01, learning_rate=0.001, lambda_u=0.01, lambda_v=0.01,\n",
        "          verbose=VERBOSE, seed=SEED, name=f\"WMF(K={K})\")\n",
        "\n",
        "rs = RatioSplit(data, test_size=0.2, seed=SEED, verbose=VERBOSE)\n",
        "cornac.Experiment(eval_method=rs, models=[bpr, wmf], metrics=eval_metrics).run()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IgDxicdzH_yD"
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      "source": [
        "BPR only tries to preserve the ordinal constraints without learning to predict the rating values.  Thus, RMSE is not the right metric to evaluate BPR model.   Minimizing the loss function of BPR is analogous to maximizing AUC, therefore, we expect BPR to do well on that metric.\n",
        "\n",
        "Both BPR and WMF models are designed to obtain good performances in terms of ranking metrics.  With reasonable efforts for hyper-parameter tuning, we should see comparable performance between the two models.  "
      ]
    },
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      "cell_type": "markdown",
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        "id": "0V4hcvmFzlUw"
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      "source": [
        "## 4. Interpreting Latent Factors\n",
        "\n",
        "Both presented models (WMF and BPR) are based on the matrix factorization technique, let us see how the latent factors could give us some information about how items are associated with one another, as well as which items tend to be associated with a particular factor."
      ]
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data from http://files.grouplens.org/datasets/movielens/ml-100k/u.item\n",
            "will be cached into /root/.cornac/u.item\n"
          ]
        },
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          "text": [
            "File cached!\n"
          ]
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      ],
      "source": [
        "# Download some information of MovieLens 100K dataset\n",
        "item_df = pd.read_csv(\n",
        "  cache(\"http://files.grouplens.org/datasets/movielens/ml-100k/u.item\"), \n",
        "  sep=\"|\", encoding=\"ISO-8859-1\",\n",
        "  names=[\"ItemID\", \"Title\", \"Release Date\", \"Video Release Date\", \"IMDb URL\", \n",
        "         \"unknown\", \"Action\", \"Adventure\", \"Animation\", \"Children's\", \"Comedy\", \n",
        "         \"Crime\", \"Documentary\", \"Drama\", \"Fantasy\", \"Film-Noir\", \"Horror\", \n",
        "         \"Musical\", \"Mystery\", \"Romance\", \"Sci-Fi\", \"Thriller\", \"War\", \"Western\"]\n",
        ").set_index(\"ItemID\").drop(columns=[\"Video Release Date\", \"IMDb URL\", \"unknown\"])"
      ]
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      "cell_type": "markdown",
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      "source": [
        "### WMF Latent Factors\n",
        "\n",
        "Below is the variance of each latent dimension across different items.  If a factor or dimension has higher variance, it is better at discriminating among different items.  Conversely, if the variance of a factor is too small, it implies that most items would have a similar value for that factor."
      ]
    },
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              "<Figure size 1300x500 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "var_df = pd.DataFrame({\"Factor\": np.arange(K), \"Variance\": np.var(wmf.V, axis=0)})\n",
        "fig, ax = plt.subplots(figsize=(13, 5))\n",
        "sns.barplot(x=\"Factor\", y=\"Variance\", data=var_df, palette=\"ch:.25\", ax=ax);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lv07yu0Hrygu",
        "outputId": "a9f2b0de-614c-4f99-fe78-30330f88ce4f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x1100 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "TOP2F = (0, 18)\n",
        "SAMPLE_SIZE = 20\n",
        "\n",
        "rng = np.random.RandomState(SEED)\n",
        "sample_inds = rng.choice(np.arange(wmf.V.shape[0]), size=SAMPLE_SIZE, replace=False)\n",
        "sample_df = pd.DataFrame(data=wmf.V[sample_inds][:, TOP2F], columns=[\"x\", \"y\"])\n",
        "\n",
        "sns.lmplot(x=\"x\", y=\"y\", data=sample_df, height=11.0, fit_reg=False)\n",
        "item_idx2id = list(wmf.train_set.item_ids)\n",
        "titles = item_df.loc[[int(item_idx2id[i]) for i in sample_inds]][\"Title\"].values\n",
        "adjust_text(\n",
        "  [plt.text(*sample_df.loc[i].values, titles[i], size=10) for i in range(len(titles))]\n",
        ");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fdy0_72dTzSR"
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      "source": [
        "### Interpreting Recommendations of WMF\n",
        "\n",
        "Let's pick a user and see what the top-K recommendations by WMF model are.  "
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "TOP 5 RECOMMENDATIONS:\n",
            "Scores: [4.513543  4.466046  4.317133  4.2535644 4.2024813]\n"
          ]
        },
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              "                                   Title Release Date  Action  Adventure  \\\n",
              "ItemID                                                                     \n",
              "318              Schindler's List (1993)  01-Jan-1993       0          0   \n",
              "50                      Star Wars (1977)  01-Jan-1977       1          1   \n",
              "496         It's a Wonderful Life (1946)  01-Jan-1946       0          0   \n",
              "64      Shawshank Redemption, The (1994)  01-Jan-1994       0          0   \n",
              "169           Wrong Trousers, The (1993)  01-Jan-1993       0          0   \n",
              "\n",
              "        Animation  Children's  Comedy  Crime  Documentary  Drama  Fantasy  \\\n",
              "ItemID                                                                      \n",
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              "        Film-Noir  Horror  Musical  Mystery  Romance  Sci-Fi  Thriller  War  \\\n",
              "ItemID                                                                        \n",
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          "metadata": {},
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        }
      ],
      "source": [
        "UIDX = 0\n",
        "TOPK = 5\n",
        "\n",
        "item_id2idx = wmf.train_set.iid_map\n",
        "item_idx2id = list(wmf.train_set.item_ids)\n",
        "\n",
        "recommendations, scores = wmf.rank(UIDX)\n",
        "print(f\"\\nTOP {TOPK} RECOMMENDATIONS:\")\n",
        "print(\"Scores:\", scores[recommendations[:TOPK]])\n",
        "item_df.loc[[int(item_idx2id[i]) for i in recommendations[:TOPK]]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zluraHjsc7x-"
      },
      "source": [
        "The solution of alternating least squares model can be used to explain the recommendations.  Please refer to Section 5 in the [paper](http://yifanhu.net/PUB/cf.pdf) for more details.  In summary, the predicted preference of user $i$ for item $j$ is rewritten as:\n",
        "\n",
        "$$ \\hat{p}_{i,j} = \\sum_{l=1}^M sim_i (j, l) c_{i, l} p_{i,l} $$\n",
        "\n",
        "where $sim_i (j, l)$ is the similarity between item $j$ and item $l$ from user $i$’s viewpoint.\n",
        "\n",
        "\n",
        "Let's try to explain why the WMF model recommends the movie **Star Wars (1977)**, *ItemID* = 50 to the user."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
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        "id": "ph220lOYST3Z",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recommended movie: Star Wars (1977)\n",
            "\n",
            "Top contributors:\n"
          ]
        },
        {
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              "   ItemID                        Title  Similarity  Confidence  Adoption  \\\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-95f758af-85ce-4305-86de-790917736573 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-95f758af-85ce-4305-86de-790917736573');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "REC_ID = 50\n",
        "print(\"Recommended movie:\", item_df.loc[REC_ID].Title)\n",
        "rec_idx = item_id2idx[str(REC_ID)]\n",
        "\n",
        "U, V, lbd = wmf.U, wmf.V, wmf.lambda_v\n",
        "pi = wmf.train_set.matrix[UIDX].A.ravel()\n",
        "ci = (pi > 0) * (wmf.a - wmf.b) + wmf.b\n",
        "\n",
        "Wu = np.linalg.inv(V.T.dot(np.diag(ci)).dot(V) + lbd * np.eye(wmf.k))\n",
        "sim = V[rec_idx].dot(Wu).dot(V.T)\n",
        "contribution = sim * ci * pi\n",
        "\n",
        "print(\"\\nTop contributors:\")\n",
        "df = defaultdict(list)\n",
        "for idx in np.argsort(contribution)[-TOPK:][::-1]:\n",
        "  df[\"ItemID\"].append(item_df.loc[[int(item_idx2id[idx])]].index.values[0])\n",
        "  df[\"Title\"].append(item_df.loc[[int(item_idx2id[idx])]][\"Title\"].values[0])\n",
        "  df[\"Similarity\"].append(sim[idx])\n",
        "  df[\"Confidence\"].append(ci[idx])\n",
        "  df[\"Adoption\"].append(pi[idx])\n",
        "  df[\"Contribution\"].append(contribution[idx])\n",
        "pd.DataFrame.from_dict(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i-Mp8q_Ut_mP"
      },
      "source": [
        "### BPR Latent Factors\n",
        "\n",
        "Below is the variance of each latent dimension."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "WC7PRQVxthL_",
        "outputId": "15ece482-de60-4875-b0a5-80f4a4574500"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "var_df = pd.DataFrame({\"Factor\": np.arange(K), \"Variance\": np.var(bpr.i_factors, axis=0)})\n",
        "fig, ax = plt.subplots(figsize=(13, 5))\n",
        "sns.barplot(x=\"Factor\", y=\"Variance\", data=var_df, palette=\"ch:.25\", ax=ax);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NnnDUUcjMQbx",
        "outputId": "9330499a-74a8-4d85-fc03-3c451c84c743"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x1100 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "TOP2F = (24, 49)\n",
        "SAMPLE_SIZE = 20\n",
        "\n",
        "rng = np.random.RandomState(SEED)\n",
        "sample_inds = rng.choice(np.arange(bpr.i_factors.shape[0]), size=SAMPLE_SIZE, replace=False)\n",
        "sample_df = pd.DataFrame(data=bpr.i_factors[sample_inds][:, TOP2F], columns=[\"x\", \"y\"])\n",
        "\n",
        "sns.lmplot(x=\"x\", y=\"y\", data=sample_df, height=11.0, fit_reg=False)\n",
        "item_idx2id = list(bpr.train_set.item_ids)\n",
        "titles = item_df.loc[[int(item_idx2id[i]) for i in sample_inds]][\"Title\"].values\n",
        "adjust_text(\n",
        "  [plt.text(*sample_df.loc[i].values, titles[i], size=10) for i in range(len(titles))]\n",
        ");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0FmiuTYiP_pB"
      },
      "source": [
        "## References\n",
        "\n",
        "1.   Aggarwal, C. C. (2016). Recommender systems (Vol. 1). Cham: Springer International Publishing.\n",
        "2.   Hu, Y., Koren, Y., & Volinsky, C. (2008, December). Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE International Conference on Data Mining (pp. 263-272). Ieee.\n",
        "3.   Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012). BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618.   \n",
        "4.   Cornac - A Comparative Framework for Multimodal Recommender Systems (https://cornac.preferred.ai/)\n",
        "5.   https://github.com/recommenders-team/recommenders/blob/main/examples/02_model_collaborative_filtering/cornac_bpr_deep_dive.ipynb\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "04_implicit_feedback.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.16"
    },
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