Jouney_Make_It_Easy_Recomendation_System.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Jouney-Make-It-Easy Recomendation System.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "juZ8lATO_63Z"
      },
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import collections\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from IPython import display\n",
        "from matplotlib import pyplot as plt\n",
        "import sklearn\n",
        "import sklearn.manifold\n",
        "import tensorflow.compat.v1 as tf\n",
        "tf.disable_v2_behavior()\n",
        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "\n",
        "# Add some convenience functions to Pandas DataFrame.\n",
        "pd.options.display.max_rows = 10\n",
        "pd.options.display.float_format = '{:.3f}'.format\n",
        "def mask(df, key, function):\n",
        "  \"\"\"Returns a filtered dataframe, by applying function to key\"\"\"\n",
        "  return df[function(df[key])]\n",
        "\n",
        "def flatten_cols(df):\n",
        "  df.columns = [' '.join(col).strip() for col in df.columns.values]\n",
        "  return df\n",
        "\n",
        "pd.DataFrame.mask = mask\n",
        "pd.DataFrame.flatten_cols = flatten_cols\n",
        "\n",
        "# Install Altair and activate its colab renderer.\n",
        "print(\"Installing Altair...\")\n",
        "!pip install git+git://github.com/altair-viz/altair.git\n",
        "import altair as alt\n",
        "alt.data_transformers.enable('default', max_rows=None)\n",
        "alt.renderers.enable('colab')\n",
        "print(\"Done installing Altair.\")\n",
        "\n",
        "# Install spreadsheets and import authentication module.\n",
        "USER_RATINGS = False\n",
        "!pip install --upgrade -q gspread\n",
        "from google.colab import auth\n",
        "import gspread\n",
        "from oauth2client.client import GoogleCredentials"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qV9oi_06NinO"
      },
      "source": [
        "users_cols = ['id', 'name']\n",
        "ratings_cols = ['user_id', 'item_id', 'point']\n",
        "item_cols = ['id', 'name']\n",
        "\n",
        "from random import randrange\n",
        "\n",
        "# user0 not voting any items\n",
        "users = pd.DataFrame(data=[[i, 'user' + str(i)] for i in range(500)], index=None, columns=users_cols)\n",
        "ratings = pd.DataFrame(data=[[randrange(1, 500), randrange(300), randrange(1, 5)] for i in range(8000)], index=None, columns=ratings_cols)\n",
        "items = pd.DataFrame(data=[[i, 'item' + str(i)] for i in range(300)], index=None, columns=item_cols)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIcyWA2MMAQV"
      },
      "source": [
        "# Utility to split the data into training and test sets.\n",
        "def split_dataframe(df, holdout_fraction=0.1):\n",
        "  \"\"\"Splits a DataFrame into training and test sets.\n",
        "  Args:\n",
        "    df: a dataframe.\n",
        "    holdout_fraction: fraction of dataframe rows to use in the test set.\n",
        "  Returns:\n",
        "    train: dataframe for training\n",
        "    test: dataframe for testing\n",
        "  \"\"\"\n",
        "  test = df.sample(frac=holdout_fraction, replace=False)\n",
        "  train = df[~df.index.isin(test.index)]\n",
        "  return train, test"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmS8x0jBBd7r"
      },
      "source": [
        "def build_rating_sparse_tensor(ratings_df):\n",
        "  indices = ratings_df[['user_id', 'item_id']].values\n",
        "  values = ratings_df['point'].values\n",
        "  return tf.SparseTensor(\n",
        "      indices=indices,\n",
        "      values=values,\n",
        "      dense_shape=[users.shape[0], items.shape[0]])"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n8D8YjwRBn68"
      },
      "source": [
        "def sparse_mean_square_error(sparse_ratings, user_embeddings, movie_embeddings):\n",
        "  \"\"\"\n",
        "  Args:\n",
        "    sparse_ratings: A SparseTensor rating matrix, of dense_shape [N, M]\n",
        "    user_embeddings: A dense Tensor U of shape [N, k] where k is the embedding\n",
        "      dimension, such that U_i is the embedding of user i.\n",
        "    movie_embeddings: A dense Tensor V of shape [M, k] where k is the embedding\n",
        "      dimension, such that V_j is the embedding of movie j.\n",
        "  Returns:\n",
        "    A scalar Tensor representing the MSE between the true ratings and the\n",
        "      model's predictions.\n",
        "  \"\"\"\n",
        "  predictions = tf.reduce_sum(\n",
        "      tf.gather(user_embeddings, sparse_ratings.indices[:, 0]) *\n",
        "      tf.gather(movie_embeddings, sparse_ratings.indices[:, 1]),\n",
        "      axis=1)\n",
        "  loss = tf.losses.mean_squared_error(sparse_ratings.values, predictions)\n",
        "  return loss"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IPP2J2_jBzOE"
      },
      "source": [
        "class CFModel(object):\n",
        "  \"\"\"Simple class that represents a collaborative filtering model\"\"\"\n",
        "  def __init__(self, embedding_vars, loss, metrics=None):\n",
        "    \"\"\"Initializes a CFModel.\n",
        "    Args:\n",
        "      embedding_vars: A dictionary of tf.Variables.\n",
        "      loss: A float Tensor. The loss to optimize.\n",
        "      metrics: optional list of dictionaries of Tensors. The metrics in each\n",
        "        dictionary will be plotted in a separate figure during training.\n",
        "    \"\"\"\n",
        "    self._embedding_vars = embedding_vars\n",
        "    self._loss = loss\n",
        "    self._metrics = metrics\n",
        "    self._embeddings = {k: None for k in embedding_vars}\n",
        "    self._session = None\n",
        "\n",
        "  @property\n",
        "  def embeddings(self):\n",
        "    \"\"\"The embeddings dictionary.\"\"\"\n",
        "    return self._embeddings\n",
        "\n",
        "  def train(self, num_iterations=100, learning_rate=1.0, plot_results=True,\n",
        "            optimizer=tf.train.GradientDescentOptimizer):\n",
        "    \"\"\"Trains the model.\n",
        "    Args:\n",
        "      iterations: number of iterations to run.\n",
        "      learning_rate: optimizer learning rate.\n",
        "      plot_results: whether to plot the results at the end of training.\n",
        "      optimizer: the optimizer to use. Default to GradientDescentOptimizer.\n",
        "    Returns:\n",
        "      The metrics dictionary evaluated at the last iteration.\n",
        "    \"\"\"\n",
        "    with self._loss.graph.as_default():\n",
        "      opt = optimizer(learning_rate)\n",
        "      train_op = opt.minimize(self._loss)\n",
        "      local_init_op = tf.group(\n",
        "          tf.variables_initializer(opt.variables()),\n",
        "          tf.local_variables_initializer())\n",
        "      if self._session is None:\n",
        "        self._session = tf.Session()\n",
        "        with self._session.as_default():\n",
        "          self._session.run(tf.global_variables_initializer())\n",
        "          self._session.run(tf.tables_initializer())\n",
        "          tf.train.start_queue_runners()\n",
        "\n",
        "    with self._session.as_default():\n",
        "      local_init_op.run()\n",
        "      iterations = []\n",
        "      metrics = self._metrics or ({},)\n",
        "      metrics_vals = [collections.defaultdict(list) for _ in self._metrics]\n",
        "\n",
        "      # Train and append results.\n",
        "      for i in range(num_iterations + 1):\n",
        "        _, results = self._session.run((train_op, metrics))\n",
        "        if (i % 10 == 0) or i == num_iterations:\n",
        "          print(\"\\r iteration %d: \" % i + \", \".join(\n",
        "                [\"%s=%f\" % (k, v) for r in results for k, v in r.items()]),\n",
        "                end='')\n",
        "          iterations.append(i)\n",
        "          for metric_val, result in zip(metrics_vals, results):\n",
        "            for k, v in result.items():\n",
        "              metric_val[k].append(v)\n",
        "\n",
        "      for k, v in self._embedding_vars.items():\n",
        "        self._embeddings[k] = v.eval()\n",
        "\n",
        "      if plot_results:\n",
        "        # Plot the metrics.\n",
        "        num_subplots = len(metrics)+1\n",
        "        fig = plt.figure()\n",
        "        fig.set_size_inches(num_subplots*10, 8)\n",
        "        for i, metric_vals in enumerate(metrics_vals):\n",
        "          ax = fig.add_subplot(1, num_subplots, i+1)\n",
        "          for k, v in metric_vals.items():\n",
        "            ax.plot(iterations, v, label=k)\n",
        "          ax.set_xlim([1, num_iterations])\n",
        "          ax.legend()\n",
        "      return results"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D-gKvc3mB13z"
      },
      "source": [
        "def build_model(ratings, embedding_dim=3, init_stddev=1.):\n",
        "  \"\"\"\n",
        "  Args:\n",
        "    ratings: a DataFrame of the ratings\n",
        "    embedding_dim: the dimension of the embedding vectors.\n",
        "    init_stddev: float, the standard deviation of the random initial embeddings.\n",
        "  Returns:\n",
        "    model: a CFModel.\n",
        "  \"\"\"\n",
        "  # Split the ratings DataFrame into train and test.\n",
        "  train_ratings, test_ratings = split_dataframe(ratings)\n",
        "  # SparseTensor representation of the train and test datasets.\n",
        "  A_train = build_rating_sparse_tensor(train_ratings)\n",
        "  A_test = build_rating_sparse_tensor(test_ratings)\n",
        "  # Initialize the embeddings using a normal distribution.\n",
        "  U = tf.Variable(tf.random_normal(\n",
        "      [A_train.dense_shape[0], embedding_dim], stddev=init_stddev))\n",
        "  V = tf.Variable(tf.random_normal(\n",
        "      [A_train.dense_shape[1], embedding_dim], stddev=init_stddev))\n",
        "  train_loss = sparse_mean_square_error(A_train, U, V)\n",
        "  test_loss = sparse_mean_square_error(A_test, U, V)\n",
        "  metrics = {\n",
        "      'train_error': train_loss,\n",
        "      'test_error': test_loss\n",
        "  }\n",
        "  embeddings = {\n",
        "      \"user_id\": U,\n",
        "      \"item_id\": V\n",
        "  }\n",
        "  return CFModel(embeddings, train_loss, [metrics])"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVy-0szEB6Es"
      },
      "source": [
        "DOT = 'dot'\n",
        "COSINE = 'cosine'\n",
        "def compute_scores(query_embedding, item_embeddings, measure=DOT):\n",
        "  \"\"\"Computes the scores of the candidates given a query.\n",
        "  Args:\n",
        "    query_embedding: a vector of shape [k], representing the query embedding.\n",
        "    item_embeddings: a matrix of shape [N, k], such that row i is the embedding\n",
        "      of item i.\n",
        "    measure: a string specifying the similarity measure to be used. Can be\n",
        "      either DOT or COSINE.\n",
        "  Returns:\n",
        "    scores: a vector of shape [N], such that scores[i] is the score of item i.\n",
        "  \"\"\"\n",
        "  u = query_embedding\n",
        "  V = item_embeddings\n",
        "  if measure == COSINE:\n",
        "    V = V / np.linalg.norm(V, axis=1, keepdims=True)\n",
        "    u = u / np.linalg.norm(u)\n",
        "  scores = u.dot(V.T)\n",
        "  return scores"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybuOVYK6B9kt"
      },
      "source": [
        "def user_recommendations(model, id, measure=DOT, exclude_rated=False, k=6):\n",
        "  scores = compute_scores(\n",
        "      model.embeddings[\"user_id\"][id], model.embeddings[\"item_id\"], measure)\n",
        "  score_key = measure + ' score'\n",
        "  df = pd.DataFrame({\n",
        "      score_key: list(scores),\n",
        "      'item_id': items['id'],\n",
        "      'name': items['name'],\n",
        "  })\n",
        "  if exclude_rated:\n",
        "    # remove items that are already rated\n",
        "    rated_items = ratings[ratings.user_id == id][\"item_id\"].values\n",
        "    df = df[df.item_id.apply(lambda item_id: item_id not in rated_items)]\n",
        "  display.display(df.sort_values([score_key], ascending=False).head(k))  \n",
        "\n",
        "def item_neighbors(model, name_substring, measure=DOT, k=6):\n",
        "  # Search for item ids that match the given substring.\n",
        "  ids =  items[items['name'].str.contains(name_substring)].index.values\n",
        "  titles = items.iloc[ids]['name'].values\n",
        "  if len(titles) == 0:\n",
        "    raise ValueError(\"Found no items with title %s\" % title_substring)\n",
        "  print(\"Nearest neighbors of : %s.\" % titles[0])\n",
        "  if len(titles) > 1:\n",
        "    print(\"[Found more than one matching item. Other candidates: {}]\".format(\n",
        "        \", \".join(titles[1:])))\n",
        "  item_id = ids[0]\n",
        "  scores = compute_scores(\n",
        "      model.embeddings[\"item_id\"][item_id], model.embeddings[\"item_id\"],\n",
        "      measure)\n",
        "  score_key = measure + ' score'\n",
        "  df = pd.DataFrame({\n",
        "      score_key: list(scores),\n",
        "      'item_id': items['id'],\n",
        "      'name': items['name']\n",
        "  })\n",
        "  display.display(df.sort_values([score_key], ascending=False).head(k))"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "h_ZkHj6IB3Pd",
        "outputId": "5631831d-2725-4ef0-cf24-6e10dad3bd3d"
      },
      "source": [
        "# Build the CF model and train it.\n",
        "model = build_model(ratings, embedding_dim=30, init_stddev=0.5)\n",
        "model.train(num_iterations=1000, learning_rate=10.)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " iteration 1000: train_error=0.030463, test_error=8.049312"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'test_error': 8.049312, 'train_error': 0.030462964}]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "vCLQWA0obwz9",
        "outputId": "5b4ff70e-7a1c-40ea-e94a-5c6d00e46c01"
      },
      "source": [
        "user_recommendations(model, 200, measure=COSINE, k=10)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "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>cosine score</th>\n",
              "      <th>item_id</th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>271</th>\n",
              "      <td>0.402</td>\n",
              "      <td>271</td>\n",
              "      <td>item271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>0.386</td>\n",
              "      <td>200</td>\n",
              "      <td>item200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>0.369</td>\n",
              "      <td>117</td>\n",
              "      <td>item117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.367</td>\n",
              "      <td>9</td>\n",
              "      <td>item9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.367</td>\n",
              "      <td>8</td>\n",
              "      <td>item8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>0.351</td>\n",
              "      <td>287</td>\n",
              "      <td>item287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>0.349</td>\n",
              "      <td>87</td>\n",
              "      <td>item87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>0.341</td>\n",
              "      <td>147</td>\n",
              "      <td>item147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>0.337</td>\n",
              "      <td>79</td>\n",
              "      <td>item79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.333</td>\n",
              "      <td>6</td>\n",
              "      <td>item6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     cosine score  item_id     name\n",
              "271         0.402      271  item271\n",
              "200         0.386      200  item200\n",
              "117         0.369      117  item117\n",
              "9           0.367        9    item9\n",
              "8           0.367        8    item8\n",
              "287         0.351      287  item287\n",
              "87          0.349       87   item87\n",
              "147         0.341      147  item147\n",
              "79          0.337       79   item79\n",
              "6           0.333        6    item6"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "ASRYxURtB_5E",
        "outputId": "fd05bcf7-6a81-41c4-d70e-c52f875ccc65"
      },
      "source": [
        "user_recommendations(model, 200, measure=COSINE, k=10, exclude_rated=True)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "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>cosine score</th>\n",
              "      <th>item_id</th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>271</th>\n",
              "      <td>0.402</td>\n",
              "      <td>271</td>\n",
              "      <td>item271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>0.386</td>\n",
              "      <td>200</td>\n",
              "      <td>item200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.367</td>\n",
              "      <td>9</td>\n",
              "      <td>item9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>0.349</td>\n",
              "      <td>87</td>\n",
              "      <td>item87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>0.341</td>\n",
              "      <td>147</td>\n",
              "      <td>item147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.333</td>\n",
              "      <td>6</td>\n",
              "      <td>item6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>0.312</td>\n",
              "      <td>128</td>\n",
              "      <td>item128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.302</td>\n",
              "      <td>30</td>\n",
              "      <td>item30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.300</td>\n",
              "      <td>22</td>\n",
              "      <td>item22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>0.300</td>\n",
              "      <td>202</td>\n",
              "      <td>item202</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     cosine score  item_id     name\n",
              "271         0.402      271  item271\n",
              "200         0.386      200  item200\n",
              "9           0.367        9    item9\n",
              "87          0.349       87   item87\n",
              "147         0.341      147  item147\n",
              "6           0.333        6    item6\n",
              "128         0.312      128  item128\n",
              "30          0.302       30   item30\n",
              "22          0.300       22   item22\n",
              "202         0.300      202  item202"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "ghEUys3FCDzU",
        "outputId": "fb353cf9-e9dc-40d5-a71a-e0b4c55d6eb8"
      },
      "source": [
        "item_neighbors(model, \"item99\", DOT)\n",
        "item_neighbors(model, \"item99\", COSINE)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nearest neighbors of : item99.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        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>dot score</th>\n",
              "      <th>item_id</th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>8.926</td>\n",
              "      <td>99</td>\n",
              "      <td>item99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>6.800</td>\n",
              "      <td>21</td>\n",
              "      <td>item21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>5.362</td>\n",
              "      <td>169</td>\n",
              "      <td>item169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>5.151</td>\n",
              "      <td>192</td>\n",
              "      <td>item192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>5.124</td>\n",
              "      <td>231</td>\n",
              "      <td>item231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>4.680</td>\n",
              "      <td>101</td>\n",
              "      <td>item101</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     dot score  item_id     name\n",
              "99       8.926       99   item99\n",
              "21       6.800       21   item21\n",
              "169      5.362      169  item169\n",
              "192      5.151      192  item192\n",
              "231      5.124      231  item231\n",
              "101      4.680      101  item101"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nearest neighbors of : item99.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "\n",
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              "        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>cosine score</th>\n",
              "      <th>item_id</th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>1.000</td>\n",
              "      <td>99</td>\n",
              "      <td>item99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.620</td>\n",
              "      <td>21</td>\n",
              "      <td>item21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>0.557</td>\n",
              "      <td>169</td>\n",
              "      <td>item169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215</th>\n",
              "      <td>0.511</td>\n",
              "      <td>215</td>\n",
              "      <td>item215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>0.502</td>\n",
              "      <td>192</td>\n",
              "      <td>item192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>0.454</td>\n",
              "      <td>231</td>\n",
              "      <td>item231</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     cosine score  item_id     name\n",
              "99          1.000       99   item99\n",
              "21          0.620       21   item21\n",
              "169         0.557      169  item169\n",
              "215         0.511      215  item215\n",
              "192         0.502      192  item192\n",
              "231         0.454      231  item231"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "ITphFhfpgmTn",
        "outputId": "072db74b-a107-423a-b4f3-e123136cbb31"
      },
      "source": [
        "user_recommendations(model, 0, measure=COSINE, k=10)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "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>cosine score</th>\n",
              "      <th>item_id</th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>0.571</td>\n",
              "      <td>51</td>\n",
              "      <td>item51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>0.437</td>\n",
              "      <td>124</td>\n",
              "      <td>item124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>0.404</td>\n",
              "      <td>154</td>\n",
              "      <td>item154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>0.399</td>\n",
              "      <td>93</td>\n",
              "      <td>item93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>0.375</td>\n",
              "      <td>152</td>\n",
              "      <td>item152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>0.370</td>\n",
              "      <td>191</td>\n",
              "      <td>item191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0.356</td>\n",
              "      <td>47</td>\n",
              "      <td>item47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>0.348</td>\n",
              "      <td>280</td>\n",
              "      <td>item280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>0.347</td>\n",
              "      <td>128</td>\n",
              "      <td>item128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>0.343</td>\n",
              "      <td>287</td>\n",
              "      <td>item287</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     cosine score  item_id     name\n",
              "51          0.571       51   item51\n",
              "124         0.437      124  item124\n",
              "154         0.404      154  item154\n",
              "93          0.399       93   item93\n",
              "152         0.375      152  item152\n",
              "191         0.370      191  item191\n",
              "47          0.356       47   item47\n",
              "280         0.348      280  item280\n",
              "128         0.347      128  item128\n",
              "287         0.343      287  item287"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}