KGPR_Collaborative.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yDU1Gv-3wfSF"
   },
   "source": [
    "# KGBR - Collaborative-Based\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "collapsed": true,
    "id": "PMfpyYpsWVPs",
    "outputId": "c0843a27-f780-4c85-b3ee-ed4d7d289045"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.0 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m33.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/302.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.0/302.0 kB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.4 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m167.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m64.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/177.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.2/177.2 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.5/106.5 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m408.7/408.7 kB\u001b[0m \u001b[31m21.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m67.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.5/49.5 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade --quiet langchain langchain-groq neo4j langchain-community py2neo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Uu17tItJbjLf"
   },
   "source": [
    "Library Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zbzIGn1_1Dz2"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from langchain_community.graphs import Neo4jGraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oe_pWByQQUo4"
   },
   "outputs": [],
   "source": [
    "# Neo4j Configuration\n",
    "NEO4J_URI=\"Your URL\"\n",
    "NEO4J_USERNAME=\"Your username\"\n",
    "NEO4J_PASSWORD=\"Your Password\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5kLz_Tx8Qt-I"
   },
   "outputs": [],
   "source": [
    "os.environ[\"NEO4J_URI\"] = NEO4J_URI\n",
    "os.environ[\"NEO4J_USERNAME\"] = NEO4J_USERNAME\n",
    "os.environ[\"NEO4J_PASSWORD\"] = NEO4J_PASSWORD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mt-oJW39bs0i"
   },
   "source": [
    "Establishing the Graph Connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2UpHe6vrQ4Ap"
   },
   "outputs": [],
   "source": [
    "graph = Neo4jGraph(\n",
    "    url=os.environ.get(\"NEO4J_URI\"),\n",
    "    username=os.environ.get(\"NEO4J_USERNAME\"),\n",
    "    password=os.environ.get(\"NEO4J_PASSWORD\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZJtqO4YuR3fe",
    "outputId": "5f7c45a5-35b2-44cb-cb18-d48b69480d75"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<langchain_community.graphs.neo4j_graph.Neo4jGraph at 0x79de11645390>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SykXTfZIb8iG"
   },
   "source": [
    "**Loading Dataset**\n",
    "\n",
    "*   Movies Rating Data - Preprocessed data (https://colab.research.google.com/drive/1zQmW2qzzWyx78aoSb6jd6thKtdbWNVX5#scrollTo=Ee78UyY97xuB)\n",
    "*   Movie Genre Data - Preprocessed data (https://colab.research.google.com/drive/1zQmW2qzzWyx78aoSb6jd6thKtdbWNVX5#scrollTo=Ee78UyY97xuB)\n",
    "*   User Ratings Data - MovieLens 1M (\n",
    "https://grouplens.org/datasets/movielens/)\n",
    "\n"
   ]
  },
  {
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    "colab": {
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     "height": 493
    },
    "id": "s7eKH2ppLpuc",
    "outputId": "bc8901d3-d6df-4062-887d-80726747ffc9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1PKfUwtbcKqvBdVK_UeSsZNcCNWpmQvdZ\n",
      "To: /content/movies_rating.csv\n",
      "\r  0% 0.00/202k [00:00<?, ?B/s]\r100% 202k/202k [00:00<00:00, 124MB/s]\n"
     ]
    },
    {
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       "summary": "{\n  \"name\": \"movies_rating\",\n  \"rows\": 500,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 145,\n        \"min\": 1,\n        \"max\": 503,\n        \"num_unique_values\": 500,\n        \"samples\": [\n          365,\n          74,\n          378\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 500,\n        \"samples\": [\n          \"Little Buddha (1993)\",\n          \"Bed of Roses (1996)\",\n          \"Speechless (1994)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year_group\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.745234658946746,\n        \"min\": 0.0,\n        \"max\": 4.75,\n        \"num_unique_values\": 439,\n        \"samples\": [\n          3.7588652482269502,\n          3.826923076923077,\n          3.275\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating_group\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0501340691198666,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5.0,\n          4.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "movies_rating"
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>year_group</th>\n",
       "      <th>rating_mean</th>\n",
       "      <th>rating_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>2</td>\n",
       "      <td>4.146846</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.201141</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.016736</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>2</td>\n",
       "      <td>2.729412</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.006757</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>499</td>\n",
       "      <td>Mr. Wonderful (1993)</td>\n",
       "      <td>2</td>\n",
       "      <td>2.755102</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>500</td>\n",
       "      <td>Mrs. Doubtfire (1993)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.420048</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>501</td>\n",
       "      <td>Naked (1993)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.606383</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>502</td>\n",
       "      <td>Next Karate Kid, The (1994)</td>\n",
       "      <td>2</td>\n",
       "      <td>1.935484</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>503</td>\n",
       "      <td>New Age, The (1994)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.692308</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-5e536d35-6ee0-4b0a-a15d-b72eb50ee891 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "  <div id=\"id_6eeb0632-622a-4772-b895-f2ca74e55339\">\n",
       "    <style>\n",
       "      .colab-df-generate {\n",
       "        background-color: #E8F0FE;\n",
       "        border: none;\n",
       "        border-radius: 50%;\n",
       "        cursor: pointer;\n",
       "        display: none;\n",
       "        fill: #1967D2;\n",
       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate: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-generate {\n",
       "        background-color: #3B4455;\n",
       "        fill: #D2E3FC;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate: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",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('movies_rating')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_6eeb0632-622a-4772-b895-f2ca74e55339 button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('movies_rating');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "      id                               title  year_group  rating_mean  \\\n",
       "0      1                    Toy Story (1995)           2     4.146846   \n",
       "1      2                      Jumanji (1995)           2     3.201141   \n",
       "2      3             Grumpier Old Men (1995)           2     3.016736   \n",
       "3      4            Waiting to Exhale (1995)           2     2.729412   \n",
       "4      5  Father of the Bride Part II (1995)           2     3.006757   \n",
       "..   ...                                 ...         ...          ...   \n",
       "495  499                Mr. Wonderful (1993)           2     2.755102   \n",
       "496  500               Mrs. Doubtfire (1993)           2     3.420048   \n",
       "497  501                        Naked (1993)           2     3.606383   \n",
       "498  502         Next Karate Kid, The (1994)           2     1.935484   \n",
       "499  503                 New Age, The (1994)           2     3.692308   \n",
       "\n",
       "     rating_group  \n",
       "0             5.0  \n",
       "1             4.0  \n",
       "2             3.0  \n",
       "3             3.0  \n",
       "4             3.0  \n",
       "..            ...  \n",
       "495           2.0  \n",
       "496           4.0  \n",
       "497           2.0  \n",
       "498           3.0  \n",
       "499           2.0  \n",
       "\n",
       "[500 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!gdown 'https://drive.google.com/uc?id=1PKfUwtbcKqvBdVK_UeSsZNcCNWpmQvdZ'\n",
    "movies_rating = pd.read_csv('movies_rating.csv')\n",
    "movies_rating = movies_rating.head(500)\n",
    "movies_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 877
    },
    "collapsed": true,
    "id": "4xUtRJngKZB3",
    "outputId": "bce74a70-033f-4352-a6b8-b9799f15f847"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1DF2lsP81Rw17ktxNP6dKUmKk0TSvNTnW\n",
      "To: /content/movies_genres.csv\n",
      "\r  0% 0.00/266k [00:00<?, ?B/s]\r100% 266k/266k [00:00<00:00, 115MB/s]\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "variable_name": "movies_genre"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-fd33f29d-7467-4901-a130-5da4ef5c2b29\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Drama</th>\n",
       "      <th>...</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>(no genres listed)</th>\n",
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       "      <td>Jumanji (1995)</td>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 21 columns</p>\n",
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       "\n",
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       "\n",
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       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "    (() => {\n",
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       "        document.querySelector('#df-deecb2be-40d2-4cc5-88f2-eb896c568be2 button');\n",
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       "  </script>\n",
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       "\n",
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       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate:hover {\n",
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       "\n",
       "      [theme=dark] .colab-df-generate {\n",
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       "      [theme=dark] .colab-df-generate:hover {\n",
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       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('movies_genre')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "    </button>\n",
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       "      buttonEl.style.display =\n",
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       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('movies_genre');\n",
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       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "      id                               title  Action  Adventure  Animation  \\\n",
       "0      1                    Toy Story (1995)       0          0          1   \n",
       "1      2                      Jumanji (1995)       0          1          0   \n",
       "2      3             Grumpier Old Men (1995)       0          0          0   \n",
       "3      4            Waiting to Exhale (1995)       0          0          0   \n",
       "4      5  Father of the Bride Part II (1995)       0          0          0   \n",
       "..   ...                                 ...     ...        ...        ...   \n",
       "495  499                Mr. Wonderful (1993)       0          0          0   \n",
       "496  500               Mrs. Doubtfire (1993)       0          0          0   \n",
       "497  501                        Naked (1993)       0          0          0   \n",
       "498  502         Next Karate Kid, The (1994)       1          0          0   \n",
       "499  503                 New Age, The (1994)       0          0          0   \n",
       "\n",
       "     Children  Comedy  Crime  Documentary  Drama  ...  Film-Noir  Horror  \\\n",
       "0           0       1      0            0      0  ...          0       0   \n",
       "1           0       0      0            0      0  ...          0       0   \n",
       "2           0       1      0            0      0  ...          0       0   \n",
       "3           0       1      0            0      1  ...          0       0   \n",
       "4           0       1      0            0      0  ...          0       0   \n",
       "..        ...     ...    ...          ...    ...  ...        ...     ...   \n",
       "495         0       1      0            0      0  ...          0       0   \n",
       "496         0       1      0            0      0  ...          0       0   \n",
       "497         0       0      0            0      1  ...          0       0   \n",
       "498         0       0      0            0      0  ...          0       0   \n",
       "499         0       0      0            0      1  ...          0       0   \n",
       "\n",
       "     Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western  \\\n",
       "0          0        0        0       0         0    0        0   \n",
       "1          0        0        0       0         0    0        0   \n",
       "2          0        0        1       0         0    0        0   \n",
       "3          0        0        0       0         0    0        0   \n",
       "4          0        0        0       0         0    0        0   \n",
       "..       ...      ...      ...     ...       ...  ...      ...   \n",
       "495        0        0        1       0         0    0        0   \n",
       "496        0        0        0       0         0    0        0   \n",
       "497        0        0        0       0         0    0        0   \n",
       "498        0        0        0       0         0    0        0   \n",
       "499        0        0        0       0         0    0        0   \n",
       "\n",
       "     (no genres listed)  \n",
       "0                     0  \n",
       "1                     0  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     0  \n",
       "..                  ...  \n",
       "495                   0  \n",
       "496                   0  \n",
       "497                   0  \n",
       "498                   0  \n",
       "499                   0  \n",
       "\n",
       "[500 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!gdown 'https://drive.google.com/uc?id=1DF2lsP81Rw17ktxNP6dKUmKk0TSvNTnW'\n",
    "movies_genres = pd.read_csv('movies_genres.csv')\n",
    "movies_genre = movies_genres.head(500)\n",
    "selected_movie_ids = movies_genre['id'].unique()\n",
    "movies_genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 493
    },
    "collapsed": true,
    "id": "4P2Vm78YMum2",
    "outputId": "b1007079-2a12-4059-bb1e-cded2554d143"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=10Ls4AOmNg-ACgzoAb5eUG4CVvoeE0ljn\n",
      "To: /content/ratings.dat\n",
      "100% 24.6M/24.6M [00:00<00:00, 61.0MB/s]\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"filtered_ratings\",\n  \"rows\": 1879,\n  \"fields\": [\n    {\n      \"column\": \"user_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 1,\n        \"max\": 103,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          86,\n          54,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 155,\n        \"min\": 1,\n        \"max\": 503,\n        \"num_unique_values\": 342,\n        \"samples\": [\n          305,\n          481,\n          441\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ratings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"timestamp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6887938,\n        \"min\": 977554027,\n        \"max\": 1043799114,\n        \"num_unique_values\": 1594,\n        \"samples\": [\n          977975029,\n          977903819,\n          977851476\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "filtered_ratings"
      },
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       "\n",
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       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('filtered_ratings')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
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       "\n",
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       "  </div>\n"
      ],
      "text/plain": [
       "       user_id  movie_id  ratings  timestamp\n",
       "25           1        48        5  978824351\n",
       "39           1       150        5  978301777\n",
       "40           1         1        5  978824268\n",
       "44           1       260        4  978300760\n",
       "73           2       434        2  978300174\n",
       "...        ...       ...      ...        ...\n",
       "13319      103        89        4  977555148\n",
       "13324      103       480        5  977554196\n",
       "13325      103       485        2  977555434\n",
       "13326      103        95        4  977555434\n",
       "13328      103       494        4  977555198\n",
       "\n",
       "[1879 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!gdown 'https://drive.google.com/uc?id=10Ls4AOmNg-ACgzoAb5eUG4CVvoeE0ljn'\n",
    "column_names = ['user_id', 'movie_id', 'ratings', 'timestamp']\n",
    "df = pd.read_csv('ratings.dat', sep = '::', names = column_names, engine='python')\n",
    "filtered_ratings = df[df['movie_id'].isin(selected_movie_ids)]\n",
    "selected_user_ids = filtered_ratings['user_id'].unique()[:100]  # Select first 100 unique users\n",
    "filtered_ratings = filtered_ratings[filtered_ratings['user_id'].isin(selected_user_ids)]\n",
    "filtered_ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IjW_J-BId8BV"
   },
   "source": [
    "**Neo4j Query Execution Helper Function** - A Neo4j driver is created to handle database connections. The run_query function is defined to execute Cypher queries in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "k5fYbpJDXATb"
   },
   "outputs": [],
   "source": [
    "from neo4j import GraphDatabase\n",
    "driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))\n",
    "def run_query(query, parameters=None):\n",
    "    with driver.session() as session:\n",
    "        session.run(query, parameters=parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u8X-dhIEeKpM"
   },
   "source": [
    "**Data Loading Functions**\n",
    "1. Load nodes to the function to neo4j.\n",
    "  1. Load Movies\n",
    "  2. Load Genres\n",
    "  3. Load Ratings\n",
    "\n",
    "  \n",
    "2. Creating Similarity Relationships\n",
    "  1. User Similarity\n",
    "  2. Movie Similarity by Genre\n",
    "  3. Movie Similarity by Year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yn2cmtm1kb9V"
   },
   "outputs": [],
   "source": [
    "def load_movies(movies_rating):\n",
    "    # Load movie nodes with properties: id, title, year_group, rating_mean\n",
    "    for _, row in movies_rating.iterrows():\n",
    "        query = \"\"\"\n",
    "        MERGE (m:Movie {id: $id})\n",
    "        SET m.title = $title,\n",
    "            m.year_group = $year_group,\n",
    "            m.rating_mean = $rating_mean\n",
    "        \"\"\"\n",
    "        parameters = {\n",
    "            \"id\": row['id'],\n",
    "            \"title\": row['title'],\n",
    "            \"year_group\": int(row['year_group']),\n",
    "            \"rating_mean\": float(row['rating_mean'])\n",
    "        }\n",
    "        run_query(query, parameters)\n",
    "    print(\"Movies with titles loaded successfully.\")\n",
    "\n",
    "\n",
    "\n",
    "def load_genres(movies_genre):\n",
    "    genre_columns = ['Action', 'Adventure', 'Animation', 'Children', 'Comedy', 'Crime', 'Documentary',\n",
    "                     'Drama', 'Fantasy', 'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance',\n",
    "                     'Sci-Fi', 'Thriller', 'War', 'Western']\n",
    "\n",
    "    for _, row in movies_genre.iterrows():\n",
    "        for genre in genre_columns:\n",
    "            if row[genre] == 1:\n",
    "                query = \"\"\"\n",
    "                MERGE (m:Movie {id: $id})\n",
    "                MERGE (g:Genre {name: $genre})\n",
    "                MERGE (m)-[:HAS_GENRE]->(g)\n",
    "                \"\"\"\n",
    "                parameters = {\"id\": row['id'], \"genre\": genre}\n",
    "                run_query(query, parameters)\n",
    "    print(\"Genres and genre relationships loaded successfully.\")\n",
    "\n",
    "\n",
    "\n",
    "def load_ratings(filtered_ratings):\n",
    "    for _, row in filtered_ratings.iterrows():\n",
    "        query = \"\"\"\n",
    "        MERGE (u:User {id: $user_id})\n",
    "        MERGE (m:Movie {id: $movie_id})\n",
    "        CREATE (u)-[:RATES {rating: $rating, timestamp: $timestamp}]->(m)\n",
    "        \"\"\"\n",
    "        parameters = {\n",
    "            \"user_id\": row['user_id'],\n",
    "            \"movie_id\": row['movie_id'],\n",
    "            \"rating\": float(row['ratings']),\n",
    "            \"timestamp\": int(row['timestamp'])\n",
    "        }\n",
    "        run_query(query, parameters)\n",
    "    print(\"Ratings loaded successfully.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QutqQTesmqDk",
    "outputId": "7113ca49-bad0-4afe-cd58-42c4112e05f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movies with titles loaded successfully.\n",
      "Genres and genre relationships loaded successfully.\n",
      "Ratings loaded successfully.\n"
     ]
    }
   ],
   "source": [
    "load_movies(movies_rating)\n",
    "load_genres(movies_genre)\n",
    "load_ratings(filtered_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "seICW0eNsRcR"
   },
   "outputs": [],
   "source": [
    "def create_user_similarity_relationship(graph, rating_threshold=0.5):\n",
    "    query = f\"\"\"\n",
    "    MATCH (u1:User)-[r1:RATES]->(m:Movie)<-[r2:RATES]-(u2:User)\n",
    "    WHERE u1 <> u2 AND\n",
    "          abs(r1.rating - r2.rating) <= {rating_threshold}\n",
    "    MERGE (u1)-[:SIMILAR_TO_Ratings]->(u2)\n",
    "    \"\"\"\n",
    "    try:\n",
    "        graph.query(query)\n",
    "        print(\"User similarity relationships created with constraints.\")\n",
    "    except Exception as e:\n",
    "        print(\"An error occurred while creating user similarity relationships:\", e)\n",
    "\n",
    "\n",
    "\n",
    "def create_movie_similarity_relationship_by_genre(graph, genre_threshold=2, rating_diff_threshold=2):\n",
    "    query = f\"\"\"\n",
    "    MATCH (m1:Movie)-[:HAS_GENRE]->(g:Genre)<-[:HAS_GENRE]-(m2:Movie)\n",
    "    WHERE m1 <> m2 AND\n",
    "          abs(m1.rating_mean - m2.rating_mean) <= {rating_diff_threshold}\n",
    "    WITH m1, m2, COUNT(g) AS common_genres\n",
    "    WHERE common_genres >= {genre_threshold}\n",
    "    MERGE (m1)-[:SIMILAR_TO_GENRE {{score: common_genres}}]->(m2)\n",
    "    \"\"\"\n",
    "    try:\n",
    "        graph.query(query)\n",
    "        print(\"Movie similarity relationships based on genre created successfully.\")\n",
    "    except Exception as e:\n",
    "        print(\"An error occurred while creating movie similarity relationships by genre:\", e)\n",
    "\n",
    "def create_movie_similarity_relationship_by_year(graph, year_threshold=1, genre_threshold=2):\n",
    "    query = f\"\"\"\n",
    "    MATCH (m1:Movie)-[:HAS_GENRE]->(g:Genre)<-[:HAS_GENRE]-(m2:Movie)\n",
    "    WHERE m1 <> m2 AND\n",
    "          abs(m1.year_group - m2.year_group) <= {year_threshold}\n",
    "    WITH m1, m2, COUNT(g) AS common_genres\n",
    "    WHERE common_genres >= {genre_threshold}\n",
    "    MERGE (m1)-[:SIMILAR_TO_YEAR {{score: common_genres}}]->(m2)\n",
    "    \"\"\"\n",
    "    try:\n",
    "        graph.query(query)\n",
    "        print(\"Movie similarity relationships based on year created successfully.\")\n",
    "    except Exception as e:\n",
    "        print(\"An error occurred while creating movie similarity relationships by year:\", e)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NEh8VHShvFiW",
    "outputId": "6d8bf59e-b9df-4d34-c57e-abdd394c39a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User similarity relationships created with constraints.\n"
     ]
    }
   ],
   "source": [
    "create_user_similarity_relationship(graph, rating_threshold=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jBz_NfaSfTcL",
    "outputId": "9a6280fe-cc86-49db-86c1-6d8df0b8aa35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie similarity relationships based on year created successfully.\n"
     ]
    }
   ],
   "source": [
    "create_movie_similarity_relationship_by_year(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Vgg_dhF94OtM",
    "outputId": "f74275a1-cb7a-42a8-93ef-25d47f688aff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie similarity relationships based on year created successfully.\n"
     ]
    }
   ],
   "source": [
    "create_movie_similarity_relationship_by_year(graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5PMLuPQwge2U"
   },
   "source": [
    "**Recommendations**\n",
    "1. Based on Similar Users\n",
    "2. Based on Genre and Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "F_tG1xEnu7u3"
   },
   "outputs": [],
   "source": [
    "def recommend_movies_based_on_similar_users(graph, user_id, top_n=10):\n",
    "    query = f\"\"\"\n",
    "    MATCH (u:User {{id: {user_id}}})-[:SIMILAR_TO_Ratings]-(similar_user:User)-[r:RATES]->(m:Movie)\n",
    "    WHERE NOT (u)-[:RATES]->(m)\n",
    "    RETURN m.title AS movie_title, COLLECT(DISTINCT similar_user.id) AS similar_users, AVG(r.rating) AS average_rating\n",
    "    ORDER BY average_rating DESC\n",
    "    LIMIT {top_n}\n",
    "    \"\"\"\n",
    "\n",
    "    try:\n",
    "        recommendations = graph.query(query)\n",
    "        print(f\"Movies recommended based on similar users for user {user_id}:\")\n",
    "        if not recommendations:\n",
    "            print(\"No recommendations found.\")\n",
    "        for rec in recommendations:\n",
    "            print(f\"Title: {rec['movie_title']} \\t| Average Rating: {rec['average_rating']} \\t| Similar Users: {rec['similar_users']}\")\n",
    "    except Exception as e:\n",
    "        print(\"An error occurred while fetching recommendations:\", e)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UmgcJPdM4hLL",
    "outputId": "c3b79be9-6b8e-4200-d14c-a97dc68cf6de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movies recommended based on similar users for user 1:\n",
      "Title: Fear of a Black Hat (1993) \t| Average Rating: 5.0 \t| Similar Users: [36]\n",
      "Title: Eat Drink Man Woman (1994) \t| Average Rating: 5.0 \t| Similar Users: [36, 53]\n",
      "Title: Hoop Dreams (1994) \t| Average Rating: 5.0 \t| Similar Users: [36, 56]\n",
      "Title: Exotica (1994) \t| Average Rating: 5.0 \t| Similar Users: [36, 53]\n",
      "Title: Mr. Holland's Opus (1995) \t| Average Rating: 5.0 \t| Similar Users: [10, 82]\n",
      "Title: Umbrellas of Cherbourg, The (Parapluies de Cherbourg, Les) (1964) \t| Average Rating: 5.0 \t| Similar Users: [6, 53]\n",
      "Title: Death and the Maiden (1994) \t| Average Rating: 5.0 \t| Similar Users: [36]\n",
      "Title: Nell (1994) \t| Average Rating: 5.0 \t| Similar Users: [10, 53]\n",
      "Title: Houseguest (1994) \t| Average Rating: 5.0 \t| Similar Users: [10]\n",
      "Title: Clockers (1995) \t| Average Rating: 5.0 \t| Similar Users: [49]\n"
     ]
    }
   ],
   "source": [
    "user_id = 1\n",
    "recommend_movies_based_on_similar_users(graph, user_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hPgMxp7CgYHk"
   },
   "outputs": [],
   "source": [
    "def recommend_movies_based_on_genre_and_year(graph, movie_id, genre_threshold=1, top_n=10):\n",
    "    query = f\"\"\"\n",
    "    MATCH (m1:Movie {{id: {movie_id}}})-[:HAS_GENRE]->(g:Genre)<-[:HAS_GENRE]-(m2:Movie)\n",
    "    WHERE m1 <> m2\n",
    "    WITH m1, m2, COUNT(g) AS genre_count\n",
    "    WHERE genre_count >= {genre_threshold} AND abs(m1.year_group - m2.year_group) <= 1\n",
    "    RETURN m2.title AS movie_title\n",
    "    ORDER BY m2.title\n",
    "    LIMIT {top_n}\n",
    "    \"\"\"\n",
    "\n",
    "    try:\n",
    "        recommendations = graph.query(query)\n",
    "        print(f\"Movies recommended based on genre and year for movie {movie_id}:\")\n",
    "        for rec in recommendations:\n",
    "            print(f\"Title: {rec['movie_title']}\")\n",
    "    except Exception as e:\n",
    "        print(\"An error occurred while fetching recommendations:\", e)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Py_ja9aVhqlN",
    "outputId": "d8421ba7-05e8-4fed-a785-0c279922499a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movies recommended based on genre and year for movie 2:\n",
      "Title: Amazing Panda Adventure, The (1995)\n",
      "Title: Batman Forever (1995)\n",
      "Title: Black Beauty (1994)\n",
      "Title: Casper (1995)\n",
      "Title: City of Lost Children, The (1995)\n",
      "Title: Clear and Present Danger (1994)\n",
      "Title: Cliffhanger (1993)\n",
      "Title: Congo (1995)\n",
      "Title: Cutthroat Island (1995)\n",
      "Title: Far From Home: The Adventures of Yellow Dog (1995)\n"
     ]
    }
   ],
   "source": [
    "movieId = 2\n",
    "recommend_movies_based_on_genre_and_year(graph, movieId)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d_9zqZJ5krux"
   },
   "source": [
    "Score Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ppM6o721fIkY"
   },
   "outputs": [],
   "source": [
    "def calculate_metrics(recommended_movies, rated_movies_set, k=10):\n",
    "    # Get the top k recommended movies\n",
    "    top_k_recommendations = recommended_movies[:k]\n",
    "\n",
    "    # Calculate Precision@k\n",
    "    true_positives = len(set(top_k_recommendations) & rated_movies_set)\n",
    "    precision_at_k = true_positives / k if k > 0 else 0.0\n",
    "\n",
    "    # Calculate Recall@k\n",
    "    recall_at_k = true_positives / len(rated_movies_set) if len(rated_movies_set) > 0 else 0.0\n",
    "\n",
    "    # Calculate F1-score@k\n",
    "    if precision_at_k + recall_at_k > 0:\n",
    "        f1_at_k = 2 * (precision_at_k * recall_at_k) / (precision_at_k + recall_at_k)\n",
    "    else:\n",
    "        f1_at_k = 0.0\n",
    "\n",
    "    return precision_at_k, recall_at_k, f1_at_k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lGEvPSMvfsFI"
   },
   "outputs": [],
   "source": [
    "def recommend_movies_based_on_similar_users(graph, user_id, top_n=50):\n",
    "    query = f\"\"\"\n",
    "    MATCH (u:User {{id: {user_id}}})-[:SIMILAR_TO_Ratings]-(similar_user:User)-[r:RATES]->(m:Movie)\n",
    "    RETURN m.id AS movie_Id\n",
    "    LIMIT {top_n}\n",
    "    \"\"\"\n",
    "\n",
    "    try:\n",
    "        recommendations = graph.query(query)\n",
    "\n",
    "        if not recommendations:\n",
    "            print(\"No recommendations found.\")\n",
    "        recommended_movies = [rec['movie_Id'] for rec in recommendations]\n",
    "        print(\"Recommended movies:\", recommended_movies)\n",
    "\n",
    "\n",
    "        rated_movies_query = f\"\"\"\n",
    "        MATCH (u:User {{id: {user_id}}})-[:RATES]->(m:Movie)\n",
    "        RETURN m.id AS movie_Id\n",
    "        \"\"\"\n",
    "        rated_movies = graph.query(rated_movies_query)\n",
    "        print(rated_movies)\n",
    "\n",
    "        rated_movies_set = {rec['movie_Id'] for rec in rated_movies}\n",
    "\n",
    "        # Calculate Precision, Recall, F1-score\n",
    "        precision, recall, f1 = calculate_metrics(recommended_movies, rated_movies_set, top_n)\n",
    "\n",
    "        # Output Precision, Recall, F1-Score\n",
    "        print(f\"Precision@{top_n}: {precision}\")\n",
    "        print(f\"Recall@{top_n}: {recall}\")\n",
    "        print(f\"F1-score@{top_n}: {f1}\")\n",
    "\n",
    "    except Exception as e:\n",
    "        print(\"An error occurred while fetching recommendations:\", e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OGSEZcjrf3hr",
    "outputId": "be1b0c7e-596e-4da6-a479-af96253cfa49"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommended movies: [1, 48, 150, 260, 21]\n",
      "[{'movie_Id': 1}, {'movie_Id': 2}, {'movie_Id': 7}, {'movie_Id': 24}, {'movie_Id': 32}, {'movie_Id': 48}, {'movie_Id': 62}, {'movie_Id': 104}, {'movie_Id': 110}, {'movie_Id': 116}, {'movie_Id': 150}, {'movie_Id': 153}, {'movie_Id': 180}, {'movie_Id': 186}, {'movie_Id': 203}, {'movie_Id': 208}, {'movie_Id': 223}, {'movie_Id': 248}, {'movie_Id': 253}, {'movie_Id': 260}, {'movie_Id': 275}, {'movie_Id': 277}, {'movie_Id': 282}, {'movie_Id': 292}, {'movie_Id': 316}, {'movie_Id': 317}, {'movie_Id': 318}, {'movie_Id': 329}, {'movie_Id': 333}, {'movie_Id': 339}, {'movie_Id': 344}, {'movie_Id': 351}, {'movie_Id': 356}, {'movie_Id': 357}, {'movie_Id': 364}, {'movie_Id': 367}, {'movie_Id': 368}, {'movie_Id': 370}, {'movie_Id': 435}, {'movie_Id': 480}]\n",
      "Precision@5: 0.8\n",
      "Recall@5: 0.1\n",
      "F1-score@5: 0.1777777777777778\n"
     ]
    }
   ],
   "source": [
    "user_id = 10\n",
    "recommend_movies_based_on_similar_users(graph, user_id, top_n=5)"
   ]
  }
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