Music_Recommendation System.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Music_Recs.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uixRc0XtrHuj"
      },
      "source": [
        "# **Music Recommendation System**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h5JHtaRlpGiE",
        "outputId": "66278398-3733-42f4-a207-6cfafd10d40c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hMZ64UxxrMvL"
      },
      "source": [
        "# Install pyspark\n",
        " - PySpark for SQL manipulations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dal3Np2Imn8u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1668291-d097-4b3e-ec40-2bab2d3da233"
      },
      "source": [
        "!pip install pyspark"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyspark\n",
            "  Downloading pyspark-3.3.0.tar.gz (281.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 281.3 MB 46 kB/s \n",
            "\u001b[?25hCollecting py4j==0.10.9.5\n",
            "  Downloading py4j-0.10.9.5-py2.py3-none-any.whl (199 kB)\n",
            "\u001b[K     |████████████████████████████████| 199 kB 61.3 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pyspark\n",
            "  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyspark: filename=pyspark-3.3.0-py2.py3-none-any.whl size=281764026 sha256=93144aad0273809b478c63cdcf9c1979c5b8ea7f8100b2912f1af826d13ca549\n",
            "  Stored in directory: /root/.cache/pip/wheels/7a/8e/1b/f73a52650d2e5f337708d9f6a1750d451a7349a867f928b885\n",
            "Successfully built pyspark\n",
            "Installing collected packages: py4j, pyspark\n",
            "Successfully installed py4j-0.10.9.5 pyspark-3.3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eK9D-CNgru_s"
      },
      "source": [
        "Importing the modules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_cmzia9nA1W"
      },
      "source": [
        "from pyspark.sql import SparkSession\n",
        "from pyspark.sql.functions import count, desc , col, max\n",
        "from pyspark.ml.feature import  StringIndexer\n",
        "from pyspark.ml import Pipeline\n",
        "from pyspark.ml.recommendation import ALS\n",
        "from pyspark.ml.tuning import TrainValidationSplit, ParamGridBuilder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjM5P6b0rVpH"
      },
      "source": [
        "Creating the spark session\n",
        "give our application a name, here we used manofm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvSzI0zMn4Hk"
      },
      "source": [
        "spark = SparkSession.builder.appName(\"manofm\").getOrCreate()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KnyYdMVmnkp5"
      },
      "source": [
        "# Loading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PywJeVgDngVd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "974c4eaa-7bf8-438a-a935-849119785773"
      },
      "source": [
        "file_path = '/content/drive/MyDrive/dataset/dataset/dataset/listenings.csv'\n",
        "df_listenings = spark.read.format('csv').option('header',True).option('inferSchema',True).load(file_path) #data frame, header will infer column types from csv\n",
        "df_listenings.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+-------------+--------------------+---------------+--------------------+\n",
            "|    user_id|         date|               track|         artist|               album|\n",
            "+-----------+-------------+--------------------+---------------+--------------------+\n",
            "|000Silenced|1299680100000|           Price Tag|       Jessie J|         Who You Are|\n",
            "|000Silenced|1299679920000|Price Tag (Acoust...|       Jessie J|           Price Tag|\n",
            "|000Silenced|1299679440000|Be Mine! (Ballad ...|          Robyn|            Be Mine!|\n",
            "|000Silenced|1299679200000|            Acapella|          Kelis|            Acapella|\n",
            "|000Silenced|1299675660000|   I'm Not Invisible|      The Tease|   I'm Not Invisible|\n",
            "|000Silenced|1297511400000|Bounce (Feat NORE...|       MSTRKRFT|         Fist of God|\n",
            "|000Silenced|1294498440000|Don't Stop The Mu...|        Rihanna|Addicted 2 Bassli...|\n",
            "|000Silenced|1292438340000|               ObZen|      Meshuggah|               ObZen|\n",
            "|000Silenced|1292437740000|   Yama's Messengers|         Gojira|The Way of All Flesh|\n",
            "|000Silenced|1292436360000|On the Brink of E...|   Napalm Death|Time Waits For No...|\n",
            "|000Silenced|1292436360000|On the Brink of E...|   Napalm Death|Time Waits For No...|\n",
            "|000Silenced|1292435940000|        In Deference|   Napalm Death|      Smear Campaign|\n",
            "|000Silenced|1292434920000|      Post(?)organic|    Decapitated|Organic Hallucinosis|\n",
            "|000Silenced|1292434560000|        Mind Feeders|   Dom & Roland| No Strings Attached|\n",
            "|000Silenced|1292434320000|Necrosadistic War...|Cannibal Corpse|                Kill|\n",
            "|000Silenced|1292365560000|     Dance All Night|   Dom & Roland|          Chronology|\n",
            "|000Silenced|1292365260000|          Late Night|   Dom & Roland|          Chronology|\n",
            "|000Silenced|1292365020000|          Freak Seen|   Dom & Roland|          Chronology|\n",
            "|000Silenced|1292364720000|Paradrenasite (Hi...|   Dom & Roland|          Chronology|\n",
            "|000Silenced|1292364300000|               Rhino|   Dom & Roland|          Chronology|\n",
            "+-----------+-------------+--------------------+---------------+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YUHBP0yjoJvc"
      },
      "source": [
        "\n",
        "# Cleaning tables \n",
        "the columns’ user_id, track, artist, and album all seem relevant but date does not, so  drop it!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8luDo3HndTb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e78edb23-5f37-4467-956b-bbf05d703950"
      },
      "source": [
        "df_listenings = df_listenings.drop('date')#drop date and time col from data frame\n",
        "df_listenings.show() #has information about each user, which track, artist and album they listened to"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+--------------------+---------------+--------------------+\n",
            "|    user_id|               track|         artist|               album|\n",
            "+-----------+--------------------+---------------+--------------------+\n",
            "|000Silenced|           Price Tag|       Jessie J|         Who You Are|\n",
            "|000Silenced|Price Tag (Acoust...|       Jessie J|           Price Tag|\n",
            "|000Silenced|Be Mine! (Ballad ...|          Robyn|            Be Mine!|\n",
            "|000Silenced|            Acapella|          Kelis|            Acapella|\n",
            "|000Silenced|   I'm Not Invisible|      The Tease|   I'm Not Invisible|\n",
            "|000Silenced|Bounce (Feat NORE...|       MSTRKRFT|         Fist of God|\n",
            "|000Silenced|Don't Stop The Mu...|        Rihanna|Addicted 2 Bassli...|\n",
            "|000Silenced|               ObZen|      Meshuggah|               ObZen|\n",
            "|000Silenced|   Yama's Messengers|         Gojira|The Way of All Flesh|\n",
            "|000Silenced|On the Brink of E...|   Napalm Death|Time Waits For No...|\n",
            "|000Silenced|On the Brink of E...|   Napalm Death|Time Waits For No...|\n",
            "|000Silenced|        In Deference|   Napalm Death|      Smear Campaign|\n",
            "|000Silenced|      Post(?)organic|    Decapitated|Organic Hallucinosis|\n",
            "|000Silenced|        Mind Feeders|   Dom & Roland| No Strings Attached|\n",
            "|000Silenced|Necrosadistic War...|Cannibal Corpse|                Kill|\n",
            "|000Silenced|     Dance All Night|   Dom & Roland|          Chronology|\n",
            "|000Silenced|          Late Night|   Dom & Roland|          Chronology|\n",
            "|000Silenced|          Freak Seen|   Dom & Roland|          Chronology|\n",
            "|000Silenced|Paradrenasite (Hi...|   Dom & Roland|          Chronology|\n",
            "|000Silenced|               Rhino|   Dom & Roland|          Chronology|\n",
            "+-----------+--------------------+---------------+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rDadwmpowII",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e77f4d74-86a4-49ff-ca7c-5ac6821f8806"
      },
      "source": [
        "df_listenings = df_listenings.na.drop() # removes null values in the row\n",
        "df_listenings.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+--------------------+---------------+--------------------+\n",
            "|    user_id|               track|         artist|               album|\n",
            "+-----------+--------------------+---------------+--------------------+\n",
            "|000Silenced|           Price Tag|       Jessie J|         Who You Are|\n",
            "|000Silenced|Price Tag (Acoust...|       Jessie J|           Price Tag|\n",
            "|000Silenced|Be Mine! (Ballad ...|          Robyn|            Be Mine!|\n",
            "|000Silenced|            Acapella|          Kelis|            Acapella|\n",
            "|000Silenced|   I'm Not Invisible|      The Tease|   I'm Not Invisible|\n",
            "|000Silenced|Bounce (Feat NORE...|       MSTRKRFT|         Fist of God|\n",
            "|000Silenced|Don't Stop The Mu...|        Rihanna|Addicted 2 Bassli...|\n",
            "|000Silenced|               ObZen|      Meshuggah|               ObZen|\n",
            "|000Silenced|   Yama's Messengers|         Gojira|The Way of All Flesh|\n",
            "|000Silenced|On the Brink of E...|   Napalm Death|Time Waits For No...|\n",
            "|000Silenced|On the Brink of E...|   Napalm Death|Time Waits For No...|\n",
            "|000Silenced|        In Deference|   Napalm Death|      Smear Campaign|\n",
            "|000Silenced|      Post(?)organic|    Decapitated|Organic Hallucinosis|\n",
            "|000Silenced|        Mind Feeders|   Dom & Roland| No Strings Attached|\n",
            "|000Silenced|Necrosadistic War...|Cannibal Corpse|                Kill|\n",
            "|000Silenced|     Dance All Night|   Dom & Roland|          Chronology|\n",
            "|000Silenced|          Late Night|   Dom & Roland|          Chronology|\n",
            "|000Silenced|          Freak Seen|   Dom & Roland|          Chronology|\n",
            "|000Silenced|Paradrenasite (Hi...|   Dom & Roland|          Chronology|\n",
            "|000Silenced|               Rhino|   Dom & Roland|          Chronology|\n",
            "+-----------+--------------------+---------------+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UZr0ZxOVIYr9",
        "outputId": "5c72ea11-b8e7-41bf-9fc4-fb74d0eba85c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "rows = df_listenings.count()\n",
        "cols = len(df_listenings.columns)\n",
        "print(rows,cols)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13758905 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cyf1XITGpMWV"
      },
      "source": [
        "\n",
        "# Perform some aggregation\n",
        "To see how many times each user has listened to specific track thus understanding their preference.\n",
        "We will do this by performing aggregation to see how many times each user has listened to a specific track"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PeH7vYKEoyWa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2315bb7-6e5a-46a9-efde-d86efcab8d0a"
      },
      "source": [
        "#in order to make a model, we need to know how many times a user has listened to each song\n",
        "df_listenings_agg = df_listenings.select('user_id', 'track').groupby('user_id', 'track').agg(count('*').alias('count')).orderBy('user_id')\n",
        "df_listenings_agg.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+--------------------+-----+\n",
            "|user_id|               track|count|\n",
            "+-------+--------------------+-----+\n",
            "| --Seph|               Leloo|    1|\n",
            "| --Seph|         The Embrace|    1|\n",
            "| --Seph|          Paris 2004|    7|\n",
            "| --Seph|Chelsea Hotel - L...|    1|\n",
            "| --Seph|               Julia|    1|\n",
            "| --Seph|In the Nothing of...|    2|\n",
            "| --Seph|          I Miss You|    1|\n",
            "| --Seph| The Riders of Rohan|    1|\n",
            "| --Seph|Sunset Soon Forgo...|    1|\n",
            "| --Seph|   Barbados Carnival|    1|\n",
            "| --Seph|      Fragile Meadow|    1|\n",
            "| --Seph|          Stupid Kid|    1|\n",
            "| --Seph|Every Direction I...|    2|\n",
            "| --Seph|         If It Works|    1|\n",
            "| --Seph|           So Lonely|    2|\n",
            "| --Seph|    Kiss with a Fist|    1|\n",
            "| --Seph|             Starman|    2|\n",
            "| --Seph|         Left Behind|    2|\n",
            "| --Seph|   Duel of the Fates|    1|\n",
            "| --Seph|       Pressure Drop|    1|\n",
            "+-------+--------------------+-----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RhVz-SvapIyr",
        "outputId": "573caa59-be29-4023-8c4c-8da595ff5990",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "row = df_listenings_agg.count()\n",
        "col = len(df_listenings_agg.columns)\n",
        "print(row,col)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9930128 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We Want to decrement our row size, its almost 10 million, lets reduce it inorder to process our data much faster\n",
        "df_listenings_agg = df_listenings_agg.limit(20000)\n"
      ],
      "metadata": {
        "id": "GNJjYNL1NFL9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vuebvg7UqzsO"
      },
      "source": [
        "# Convert the user id and track columns into unique integers\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9RSpj3DN6aX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6ef914f-cd8a-45ac-e9eb-fdeb9fb4dddf"
      },
      "source": [
        "#We want to use StringIndexer to convert userID and track to unique, integer values\n",
        "#StringIndexer encodes a string column of labels to a column of label indices\n",
        "old_strindexer = [StringIndexer(inputCol = col, outputCol = col + '_index').fit(df_listenings_agg) for col in list(set(df_listenings_agg.columns)- set(['count']))]\n",
        "indexer = [curr_strindexer.setHandleInvalid(\"keep\") for curr_strindexer in old_strindexer]\n",
        "pipeline = Pipeline(stages = indexer)\n",
        "#the dataframe will be called data\n",
        "#Transformers convert one dataframe into another either by updating the current values of a particular column (like converting categorical columns to numeric) or mapping it to some other values by using a defined logic.\n",
        "#An Estimator implements the fit() method on a dataframe and produces a model.\n",
        "data = pipeline.fit(df_listenings_agg).transform(df_listenings_agg)\n",
        "data.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+--------------------+-----+-------------+-----------+\n",
            "|user_id|               track|count|user_id_index|track_index|\n",
            "+-------+--------------------+-----+-------------+-----------+\n",
            "| --Seph|          Nightmares|    1|         69.0|    10600.0|\n",
            "| --Seph|Virus (Luke Fair ...|    1|         69.0|    15893.0|\n",
            "| --Seph|Airplanes [feat H...|    1|         69.0|      521.0|\n",
            "| --Seph|Belina (Original ...|    1|         69.0|     3280.0|\n",
            "| --Seph|              Monday|    1|         69.0|      334.0|\n",
            "| --Seph|Hungarian Dance No 5|    1|         69.0|     7555.0|\n",
            "| --Seph|       Life On Mars?|    1|         69.0|     1164.0|\n",
            "| --Seph|  California Waiting|    1|         69.0|      195.0|\n",
            "| --Seph|       Phantom Pt II|    1|         69.0|     1378.0|\n",
            "| --Seph|   Summa for Strings|    1|         69.0|    13737.0|\n",
            "| --Seph|      Hour for magic|    2|         69.0|     7492.0|\n",
            "| --Seph|Hungarian Rhapsod...|    1|         69.0|     7556.0|\n",
            "| --Seph|     The Way We Were|    1|         69.0|    14958.0|\n",
            "| --Seph| Air on the G String|    1|         69.0|     2456.0|\n",
            "| --Seph|Vestido Estampado...|    1|         69.0|    15847.0|\n",
            "| --Seph|        Window Blues|    1|         69.0|     1841.0|\n",
            "| --Seph| White Winter Hymnal|    3|         69.0|       59.0|\n",
            "| --Seph|         The Embrace|    1|         69.0|    14386.0|\n",
            "| --Seph|          Paris 2004|    7|         69.0|    11311.0|\n",
            "| --Seph|Chelsea Hotel - L...|    1|         69.0|     4183.0|\n",
            "+-------+--------------------+-----+-------------+-----------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efL-hiR-q-AO"
      },
      "source": [
        "data = data.select('user_id_index', 'track_index', 'count').orderBy('user_id_index')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OfKtj2ojrKae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68e84659-dc35-44a8-e6d6-0deff37c7c57"
      },
      "source": [
        "data.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------------+-----------+-----+\n",
            "|user_id_index|track_index|count|\n",
            "+-------------+-----------+-----+\n",
            "|          0.0|    10628.0|    1|\n",
            "|          0.0|     3338.0|    1|\n",
            "|          0.0|    12168.0|    1|\n",
            "|          0.0|    11626.0|    2|\n",
            "|          0.0|    10094.0|    4|\n",
            "|          0.0|      427.0|    1|\n",
            "|          0.0|    16878.0|    1|\n",
            "|          0.0|    11722.0|    1|\n",
            "|          0.0|    15074.0|    1|\n",
            "|          0.0|     1359.0|    1|\n",
            "|          0.0|     5874.0|    1|\n",
            "|          0.0|    11184.0|    1|\n",
            "|          0.0|     2372.0|    2|\n",
            "|          0.0|    14316.0|    1|\n",
            "|          0.0|     5346.0|    1|\n",
            "|          0.0|    11194.0|    1|\n",
            "|          0.0|     2241.0|    1|\n",
            "|          0.0|     2864.0|    1|\n",
            "|          0.0|     2663.0|    4|\n",
            "|          0.0|     6064.0|    1|\n",
            "+-------------+-----------+-----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IEwviAxXsHwN"
      },
      "source": [
        "# Train and Test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DRgu2p-PsYUw"
      },
      "source": [
        "(training, test) = data.randomSplit([0.5,0.5])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M46wV6Gusdi5"
      },
      "source": [
        "# Create our Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bwPk25M3sfRu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b68ebc20-67a5-4f23-bfe1-313d9e83b182"
      },
      "source": [
        "\n",
        "\n",
        "USERID = \"user_id_index\"\n",
        "TRACK = \"track_index\"\n",
        "COUNT = \"count\"\n",
        "\n",
        "als = ALS(maxIter = 5, regParam = 0.01, userCol = USERID, itemCol = TRACK, ratingCol = COUNT)\n",
        "# Alternating Least Squares algorithm\n",
        "print(als)\n",
        "model = als.fit(training)\n",
        "\n",
        "predictions = model.transform(test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ALS_1a33121ed899\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lqBkLowzsoj8"
      },
      "source": [
        "\n",
        "# Generate top 10 Track recommendations for each user"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "00mtv7XUsZGg"
      },
      "source": [
        "recs = model.recommendForAllUsers(10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_EQnSUh3ncar",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93daaf09-aa6a-4231-d41c-5ca38a08c018"
      },
      "source": [
        "recs.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------------+--------------------+\n",
            "|user_id_index|     recommendations|\n",
            "+-------------+--------------------+\n",
            "|            0|[{4460, 17.55068}...|\n",
            "|            1|[{4460, 14.504824...|\n",
            "|            2|[{1808, 7.1139107...|\n",
            "|            3|[{3345, 9.5855255...|\n",
            "|            4|[{7847, 5.9461546...|\n",
            "|            5|[{3345, 8.19309},...|\n",
            "|            6|[{11940, 35.23830...|\n",
            "|            7|[{4460, 14.913336...|\n",
            "|            8|[{461, 11.323953}...|\n",
            "|            9|[{9498, 8.91315},...|\n",
            "|           10|[{84, 15.409278},...|\n",
            "|           11|[{7847, 11.740087...|\n",
            "|           12|[{11940, 19.40975...|\n",
            "|           13|[{7847, 7.0682983...|\n",
            "|           14|[{4460, 30.73787}...|\n",
            "|           15|[{4460, 25.294}, ...|\n",
            "|           16|[{11940, 18.11632...|\n",
            "|           17|[{16968, 9.095592...|\n",
            "|           18|[{11940, 9.351403...|\n",
            "|           19|[{11940, 43.04874...|\n",
            "+-------------+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stUaAoKpTgqT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a20a51d-36a7-4424-d264-ddd693e80ec3"
      },
      "source": [
        "recs.take(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Row(user_id_index=0, recommendations=[Row(track_index=4460, rating=17.55068016052246), Row(track_index=1694, rating=14.235849380493164), Row(track_index=120, rating=11.060724258422852), Row(track_index=11940, rating=9.11754322052002), Row(track_index=180, rating=8.364152908325195), Row(track_index=4461, rating=8.100314140319824), Row(track_index=1439, rating=7.11166524887085), Row(track_index=10815, rating=6.750261306762695), Row(track_index=524, rating=6.750261306762695), Row(track_index=14378, rating=5.52789306640625)])]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    }
  ]
}