Recommendation System/Recommendation System.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2IrL8uzdJzsi"
      },
      "source": [
        "# Book Recommendation System\n",
        "<table align=\"left\"><td>\n",
        "  <a target=\"_blank\"  href=\"https://colab.research.google.com/github/TannerGilbert/Tutorials/blob/master/Recommendation%20System/Recommendation%20System.ipynb\">\n",
        "    <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab\n",
        "  </a>\n",
        "</td><td>\n",
        "  <a target=\"_blank\"  href=\"https://github.com/TannerGilbert/Tutorials/blob/master/Recommendation%20System/Recommendation%20System.ipynb\">\n",
        "    <img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "</td></table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ptfj1KkJzsv"
      },
      "source": [
        "A recommendation system seeks to predict the rating or preference a user would give to an item given his old item ratings or preferences. Recommendation systems are used by pretty much every major company in order to enhance the quality of their services."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "1dU1K_6dJzsx"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import warnings\n",
        "\n",
        "from tensorflow.keras.layers import Input, Embedding, Flatten, Dot, Dense, Concatenate\n",
        "from tensorflow.keras.models import Model\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6MnSkRoJzs3"
      },
      "source": [
        "## Loading in data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7d-gRXHKExr",
        "outputId": "2409812d-9997-4852-9b5b-6f89c7cde9fb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 81kB 9.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 4.2MB 39.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 51kB 7.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 143kB 49.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 112kB 56.9MB/s \n",
            "\u001b[?25h  Building wheel for kaggle-cli (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for lxml (setup.py) ... \u001b[?25lerror\n",
            "\u001b[31m  ERROR: Failed building wheel for lxml\u001b[0m\n",
            "\u001b[?25h  Building wheel for PrettyTable (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pyperclip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "    Running setup.py install for lxml ... \u001b[?25l\u001b[?25herror\n",
            "\u001b[31mERROR: Command errored out with exit status 1: /usr/bin/python3 -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-sj1d4jbp/lxml/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-sj1d4jbp/lxml/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record /tmp/pip-record-k1em7gv8/install-record.txt --single-version-externally-managed --compile Check the logs for full command output.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "# Install Kaggle API\n",
        "!pip install -q kaggle\n",
        "!pip install -q kaggle-cli"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "m6mo6lrJKHlq"
      },
      "outputs": [],
      "source": [
        "# only for google colab\n",
        "import os\n",
        "os.environ['KAGGLE_USERNAME'] = \"<username>\" \n",
        "os.environ['KAGGLE_KEY'] = \"<key>\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zgW_DyhfKInJ",
        "outputId": "05c69e4e-b949-48e7-afe0-bffa261c49a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading goodbooks-10k.zip to /content\n",
            "\r  0% 0.00/11.6M [00:00<?, ?B/s]\r 86% 10.0M/11.6M [00:00<00:00, 103MB/s]\n",
            "\r100% 11.6M/11.6M [00:00<00:00, 74.3MB/s]\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d zygmunt/goodbooks-10k --unzip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "nHHNrnbvJzs3"
      },
      "outputs": [],
      "source": [
        "dataset = pd.read_csv('ratings.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zbGHMPZ-Jzs5",
        "outputId": "c47b716d-6ddd-4c28-84cd-0cb5c7683345"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>book_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1169</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1185</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   book_id  user_id  rating\n",
              "0        1      314       5\n",
              "1        1      439       3\n",
              "2        1      588       5\n",
              "3        1     1169       4\n",
              "4        1     1185       4"
            ]
          },
          "execution_count": 6,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9njnt87HJzs7",
        "outputId": "72def718-f327-423c-f876-669e24f000e0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(981756, 3)"
            ]
          },
          "execution_count": 7,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "ZNcb0llzJzs8"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train, test = train_test_split(dataset, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gSbvWaVrJzs9",
        "outputId": "7d5f1205-c370-4d33-96d9-a28185a9efe7"
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>book_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>341848</th>\n",
              "      <td>3423</td>\n",
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              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>74960</th>\n",
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              "      <td>3</td>\n",
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              "      <td>5</td>\n",
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            ],
            "text/plain": [
              "        book_id  user_id  rating\n",
              "341848     3423     4608       2\n",
              "964349     9811    36373       5\n",
              "645459     6485     2957       4\n",
              "74960       750    42400       3\n",
              "358670     3591    36886       5"
            ]
          },
          "execution_count": 9,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "n9I5LwdxJzs_",
        "outputId": "b6426ecd-3924-4ec9-9faa-4789ff623ebd"
      },
      "outputs": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>book_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
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            "text/plain": [
              "        book_id  user_id  rating\n",
              "646451     6495    19643       5\n",
              "614851     6175     8563       4\n",
              "974393     9920    52110       3\n",
              "21471       215    33864       5\n",
              "272540     2728    16587       3"
            ]
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          "execution_count": 10,
          "metadata": {
            "tags": []
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          "output_type": "execute_result"
        }
      ],
      "source": [
        "test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lRhV5EdLJztA",
        "outputId": "d38489b4-16c4-4f7e-e5d0-778abc9e5a89"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "53424"
            ]
          },
          "execution_count": 11,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "n_users = len(dataset.user_id.unique())\n",
        "n_users"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R58jC1n8JztD",
        "outputId": "1de5a17d-c95e-4afd-fc4d-88c837034ffc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "execution_count": 12,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "n_books = len(dataset.book_id.unique())\n",
        "n_books"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bcPC9c7EJztE"
      },
      "source": [
        "## Creating dot product model\n",
        "Most recommendation systems are build using a simple dot product as shown below but newer ones are now implementing a neural network instead of the simple dot product."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Cn7XrUZ3JztF"
      },
      "outputs": [],
      "source": [
        "# creating book embedding path\n",
        "book_input = Input(shape=[1], name=\"Book-Input\")\n",
        "book_embedding = Embedding(n_books+1, 5, name=\"Book-Embedding\")(book_input)\n",
        "book_vec = Flatten(name=\"Flatten-Books\")(book_embedding)\n",
        "\n",
        "# creating user embedding path\n",
        "user_input = Input(shape=[1], name=\"User-Input\")\n",
        "user_embedding = Embedding(n_users+1, 5, name=\"User-Embedding\")(user_input)\n",
        "user_vec = Flatten(name=\"Flatten-Users\")(user_embedding)\n",
        "\n",
        "# performing dot product and creating model\n",
        "prod = Dot(name=\"Dot-Product\", axes=1)([book_vec, user_vec])\n",
        "model = Model([user_input, book_input], prod)\n",
        "model.compile('adam', 'mean_squared_error')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "I3SL-ykJJztF",
        "outputId": "2f1dd98a-fd6c-4760-8aa3-b51d48ad9c41",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "24544/24544 [==============================] - 112s 4ms/step - loss: 15.3271\n",
            "Epoch 2/5\n",
            "24544/24544 [==============================] - 107s 4ms/step - loss: 6.6203\n",
            "Epoch 3/5\n",
            "24544/24544 [==============================] - 107s 4ms/step - loss: 2.2780\n",
            "Epoch 4/5\n",
            "24544/24544 [==============================] - 108s 4ms/step - loss: 1.3414\n",
            "Epoch 5/5\n",
            "24544/24544 [==============================] - 107s 4ms/step - loss: 1.0093\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from keras.models import load_model\n",
        "\n",
        "if os.path.exists('regression_model.h5'):\n",
        "    model = load_model('regression_model.h5')\n",
        "else:\n",
        "    history = model.fit([train.user_id, train.book_id], train.rating, epochs=5, verbose=1)\n",
        "    model.save('regression_model.h5')\n",
        "    plt.plot(history.history['loss'])\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(\"Training Error\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rdEVszjgJztH",
        "outputId": "fd13ca79-78d3-412e-f69a-1997c0fff9f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "6136/6136 [==============================] - 8s 1ms/step - loss: 1.2534\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "1.253379225730896"
            ]
          },
          "execution_count": 15,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.evaluate([test.user_id, test.book_id], test.rating)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZU9JB5VbJztH",
        "outputId": "8c5b6ebd-b015-4546-d1f1-32aa82a305ab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[4.9011655] 5\n",
            "[3.909601] 4\n",
            "[3.5981612] 3\n",
            "[4.3802824] 5\n",
            "[3.7667456] 3\n",
            "[3.7533305] 3\n",
            "[3.0974283] 3\n",
            "[4.841637] 4\n",
            "[3.8465247] 3\n",
            "[4.0708756] 5\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[None, None, None, None, None, None, None, None, None, None]"
            ]
          },
          "execution_count": 16,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictions = model.predict([test.user_id.head(10), test.book_id.head(10)])\n",
        "\n",
        "[print(predictions[i], test.rating.iloc[i]) for i in range(0,10)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5UGpyb7HJztI"
      },
      "source": [
        "## Creating Neural Network\n",
        "Neural Networks proved there effectivness for almost every machine learning problem as of now and they also perform exceptionally well for recommendation systems."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "tbEBEfzSJztI"
      },
      "outputs": [],
      "source": [
        "# creating book embedding path\n",
        "book_input = Input(shape=[1], name=\"Book-Input\")\n",
        "book_embedding = Embedding(n_books+1, 5, name=\"Book-Embedding\")(book_input)\n",
        "book_vec = Flatten(name=\"Flatten-Books\")(book_embedding)\n",
        "\n",
        "# creating user embedding path\n",
        "user_input = Input(shape=[1], name=\"User-Input\")\n",
        "user_embedding = Embedding(n_users+1, 5, name=\"User-Embedding\")(user_input)\n",
        "user_vec = Flatten(name=\"Flatten-Users\")(user_embedding)\n",
        "\n",
        "# concatenate features\n",
        "conc = Concatenate()([book_vec, user_vec])\n",
        "\n",
        "# add fully-connected-layers\n",
        "fc1 = Dense(128, activation='relu')(conc)\n",
        "fc2 = Dense(32, activation='relu')(fc1)\n",
        "out = Dense(1)(fc2)\n",
        "\n",
        "# Create model and compile it\n",
        "model2 = Model([user_input, book_input], out)\n",
        "model2.compile('adam', 'mean_squared_error')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "CYNU5U1JJztJ",
        "outputId": "111d885e-4b08-4926-cdf1-7d23c84d28ce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "24544/24544 [==============================] - 115s 5ms/step - loss: 0.9909\n",
            "Epoch 2/5\n",
            "24544/24544 [==============================] - 114s 5ms/step - loss: 0.6816\n",
            "Epoch 3/5\n",
            "24544/24544 [==============================] - 112s 5ms/step - loss: 0.6462\n",
            "Epoch 4/5\n",
            "24544/24544 [==============================] - 112s 5ms/step - loss: 0.6210\n",
            "Epoch 5/5\n",
            "24544/24544 [==============================] - 112s 5ms/step - loss: 0.5978\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from keras.models import load_model\n",
        "\n",
        "if os.path.exists('regression_model2.h5'):\n",
        "    model2 = load_model('regression_model2.h5')\n",
        "else:\n",
        "    history = model2.fit([train.user_id, train.book_id], train.rating, epochs=5, verbose=1)\n",
        "    model2.save('regression_model2.h5')\n",
        "    plt.plot(history.history['loss'])\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(\"Training Error\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nqiquYluJztK",
        "outputId": "4fbc4ddc-a924-455e-eb18-70f0349a0333"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "6136/6136 [==============================] - 9s 1ms/step - loss: 0.7132\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.7132264971733093"
            ]
          },
          "execution_count": 19,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model2.evaluate([test.user_id, test.book_id], test.rating)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vHgZr27oJztM",
        "outputId": "573c59dd-b0d7-4b2a-b573-d8268444835f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[5.107734] 5\n",
            "[3.902279] 4\n",
            "[3.2454526] 3\n",
            "[3.968115] 5\n",
            "[3.1826286] 3\n",
            "[4.056015] 3\n",
            "[3.8675883] 3\n",
            "[4.7206354] 4\n",
            "[4.269019] 3\n",
            "[4.1037846] 5\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[None, None, None, None, None, None, None, None, None, None]"
            ]
          },
          "execution_count": 20,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictions = model2.predict([test.user_id.head(10), test.book_id.head(10)])\n",
        "\n",
        "[print(predictions[i], test.rating.iloc[i]) for i in range(0,10)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Yi1-uJuJztN"
      },
      "source": [
        "## Visualizing Embeddings\n",
        "Embeddings are weights that are learned to represent some specific variable like books and user in our case and therefore we can not only use them to get good results on our problem but also to extract inside about our data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "AwMs9ZhpJztN"
      },
      "outputs": [],
      "source": [
        "# Extract embeddings\n",
        "book_em = model.get_layer('Book-Embedding')\n",
        "book_em_weights = book_em.get_weights()[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7rU6fxreJztN",
        "outputId": "dabbb64a-1443-4c05-dd72-f4ec424b84df"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[-0.0047317 ,  0.02763932, -0.04600314, -0.0412247 ,  0.00620728],\n",
              "       [ 1.2028387 ,  1.2857639 , -1.5278164 , -1.1005933 ,  0.8269744 ],\n",
              "       [ 0.97660714,  1.328868  , -1.3593423 , -1.3042482 ,  1.0568628 ],\n",
              "       [ 0.949213  ,  0.67221147, -1.2034689 , -0.46824247,  0.86552835],\n",
              "       [ 0.8755239 ,  1.3966527 , -1.370951  , -1.350987  ,  1.3144002 ]],\n",
              "      dtype=float32)"
            ]
          },
          "execution_count": 22,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "book_em_weights[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ileOkmE1JztO",
        "outputId": "23bd62e5-1cf6-4b97-820d-a75475f36339"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff8c0478210>"
            ]
          },
          "execution_count": 23,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.decomposition import PCA\n",
        "import seaborn as sns\n",
        "\n",
        "pca = PCA(n_components=2)\n",
        "pca_result = pca.fit_transform(book_em_weights)\n",
        "sns.scatterplot(x=pca_result[:,0], y=pca_result[:,1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGfQEU90JztP",
        "outputId": "bb7d885b-0cee-4bc7-9e79-b1eb1696601a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.99999994"
            ]
          },
          "execution_count": 24,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "book_em_weights = book_em_weights / np.linalg.norm(book_em_weights, axis = 1).reshape((-1, 1))\n",
        "book_em_weights[0][:10]\n",
        "np.sum(np.square(book_em_weights[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ppPgGErxJztP",
        "outputId": "18082a30-fa45-4078-dee7-5124ed7de7b9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff8bf991450>"
            ]
          },
          "execution_count": 25,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "pca = PCA(n_components=2)\n",
        "pca_result = pca.fit_transform(book_em_weights)\n",
        "sns.scatterplot(x=pca_result[:,0], y=pca_result[:,1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPI8dT-NJztQ",
        "outputId": "804b456a-c530-425d-cf41-e5ca42348b54"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[t-SNE] Computing 121 nearest neighbors...\n",
            "[t-SNE] Indexed 10001 samples in 0.007s...\n",
            "[t-SNE] Computed neighbors for 10001 samples in 0.443s...\n",
            "[t-SNE] Computed conditional probabilities for sample 1000 / 10001\n",
            "[t-SNE] Computed conditional probabilities for sample 2000 / 10001\n",
            "[t-SNE] Computed conditional probabilities for sample 3000 / 10001\n",
            "[t-SNE] Computed conditional probabilities for sample 4000 / 10001\n",
            "[t-SNE] Computed conditional probabilities for sample 5000 / 10001\n",
            "[t-SNE] Computed conditional probabilities for sample 6000 / 10001\n",
            "[t-SNE] Computed conditional probabilities for sample 7000 / 10001\n",
            "[t-SNE] Computed conditional probabilities for sample 8000 / 10001\n",
            "[t-SNE] Computed conditional probabilities for sample 9000 / 10001\n",
            "[t-SNE] Computed conditional probabilities for sample 10000 / 10001\n",
            "[t-SNE] Computed conditional probabilities for sample 10001 / 10001\n",
            "[t-SNE] Mean sigma: 0.048991\n",
            "[t-SNE] KL divergence after 250 iterations with early exaggeration: 74.993668\n",
            "[t-SNE] KL divergence after 300 iterations: 2.458674\n"
          ]
        }
      ],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "\n",
        "tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)\n",
        "tnse_results = tsne.fit_transform(book_em_weights)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Kp4qbq1nJztR",
        "outputId": "75446fab-5e51-4d16-f727-e3be6bb896e0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff930149290>"
            ]
          },
          "execution_count": 27,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.scatterplot(x=tnse_results[:,0], y=tnse_results[:,1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bE_PnMIQJztS"
      },
      "source": [
        "## Making Recommendations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Arkou2TJztS",
        "outputId": "a50a6739-76fc-4ea6-9b96-1ae9c22e25db"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5])"
            ]
          },
          "execution_count": 28,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Creating dataset for making recommendations for the first user\n",
        "book_data = np.array(list(set(dataset.book_id)))\n",
        "book_data[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WhBjqpdRJztT",
        "outputId": "384b1525-6346-4a3a-d9c9-918cfba90556"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1])"
            ]
          },
          "execution_count": 29,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "user = np.array([1 for i in range(len(book_data))])\n",
        "user[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-2kcsBRwJztT",
        "outputId": "00caa03b-8cd7-4213-fd39-3005daf1b120"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([5858, 7638, 6246, 9208, 8232])"
            ]
          },
          "execution_count": 30,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictions = model.predict([user, book_data])\n",
        "\n",
        "predictions = np.array([a[0] for a in predictions])\n",
        "\n",
        "recommended_book_ids = (-predictions).argsort()[:5]\n",
        "\n",
        "recommended_book_ids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SO7OzWdsJztV",
        "outputId": "63181260-24fc-43b9-e3de-370730111415"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([3.8500102, 3.5082545, 3.3832717, 3.3525784, 3.1709607],\n",
              "      dtype=float32)"
            ]
          },
          "execution_count": 31,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# print predicted scores\n",
        "predictions[recommended_book_ids]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "OWpC7GQNJztW",
        "outputId": "27c53c0e-b194-43a9-9f1c-c4b74a058adb"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <th>book_id</th>\n",
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              "      <th>isbn13</th>\n",
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              "      <th>original_title</th>\n",
              "      <th>title</th>\n",
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              "      <th>work_ratings_count</th>\n",
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              "      <td>Suzanne Collins</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>The Hunger Games</td>\n",
              "      <td>The Hunger Games (The Hunger Games, #1)</td>\n",
              "      <td>eng</td>\n",
              "      <td>4.34</td>\n",
              "      <td>4780653</td>\n",
              "      <td>4942365</td>\n",
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              "      <td>66715</td>\n",
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              "      <td>9.780440e+12</td>\n",
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              "      <td>1997.0</td>\n",
              "      <td>Harry Potter and the Philosopher's Stone</td>\n",
              "      <td>Harry Potter and the Sorcerer's Stone (Harry P...</td>\n",
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              "      <td>4.44</td>\n",
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              "      <td>41865</td>\n",
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              "      <td>226</td>\n",
              "      <td>316015849</td>\n",
              "      <td>9.780316e+12</td>\n",
              "      <td>Stephenie Meyer</td>\n",
              "      <td>2005.0</td>\n",
              "      <td>Twilight</td>\n",
              "      <td>Twilight (Twilight, #1)</td>\n",
              "      <td>en-US</td>\n",
              "      <td>3.57</td>\n",
              "      <td>3866839</td>\n",
              "      <td>3916824</td>\n",
              "      <td>95009</td>\n",
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              "      <td>https://images.gr-assets.com/books/1361039443m...</td>\n",
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              "      <td>To Kill a Mockingbird</td>\n",
              "      <td>To Kill a Mockingbird</td>\n",
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              "      <td>3198671</td>\n",
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              "      <td>1925.0</td>\n",
              "      <td>The Great Gatsby</td>\n",
              "      <td>The Great Gatsby</td>\n",
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              "      <td>3.89</td>\n",
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            ],
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              "   id  ...                                    small_image_url\n",
              "0   1  ...  https://images.gr-assets.com/books/1447303603s...\n",
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              "\n",
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          },
          "execution_count": 32,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "books = pd.read_csv('books.csv')\n",
        "books.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "khIZydfRJztW",
        "outputId": "a23ce8eb-6b66-46c6-f4a2-4c1247707081"
      },
      "outputs": [
        {
          "data": {
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              "      <td>9.780593e+12</td>\n",
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              "      <td>The Crippled God</td>\n",
              "      <td>The Crippled God (The Malazan Book of the Fall...</td>\n",
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              "      <td>2465248</td>\n",
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              "      <td>9.781598e+12</td>\n",
              "      <td>Iain M. Banks</td>\n",
              "      <td>2004.0</td>\n",
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              "      <td>The Algebraist</td>\n",
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              "      <td>Franklin Foer</td>\n",
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              "      <td>How Soccer Explains the World: An Unlikely The...</td>\n",
              "      <td>How Soccer Explains the World</td>\n",
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              "      <td>https://s.gr-assets.com/assets/nophoto/book/50...</td>\n",
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              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "execution_count": 33,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "books[books['id'].isin(recommended_book_ids)]"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Recommendation System.ipynb",
      "provenance": [],
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    "kernelspec": {
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