Movie_Recommendation_System_using_SVD.ipynb

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  "metadata": {
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      "authorship_tag": "ABX9TyO0akY6kQnPkRakIYv12FAp",
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      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/riyajaiswal25/MLProjects/blob/main/Movie_Recommendation_System_using_SVD.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Movie Recommendation System Using SVD**"
      ],
      "metadata": {
        "id": "PtR8_DdLDNPj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Importing the basic libraries***"
      ],
      "metadata": {
        "id": "J0rl78UMDVgf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "KyhKSkC6DFVN"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Importing & Parsing the dataset as ratings and movies details***"
      ],
      "metadata": {
        "id": "QgT6kmkUDe_W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ratingData = pd.io.parsers.read_csv('ratings.dat',names=['user_id','movie_id','rating','time'], engine='python', delimiter=',')\n",
        "movieData = pd.io.parsers.read_csv('movies.dat',names=['movie_id','title','genre'], engine='python', delimiter=',')\n",
        "print(ratingData)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dFZUJtrWDl9d",
        "outputId": "83fc41a6-4697-49fa-e08e-271e438273c7"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        user_id  movie_id  rating        time\n",
            "0             1        31     2.5  1260759144\n",
            "1             1      1029     3.0  1260759179\n",
            "2             1      1061     3.0  1260759182\n",
            "3             1      1129     2.0  1260759185\n",
            "4             1      1172     4.0  1260759205\n",
            "...         ...       ...     ...         ...\n",
            "99999       671      6268     2.5  1065579370\n",
            "100000      671      6269     4.0  1065149201\n",
            "100001      671      6365     4.0  1070940363\n",
            "100002      671      6385     2.5  1070979663\n",
            "100003      671      6565     3.5  1074784724\n",
            "\n",
            "[100004 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Create the ratings matrix of shape(mxu)***"
      ],
      "metadata": {
        "id": "dS4qR12i8ZgP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ratingMatrix = np.ndarray(shape=(np.max(ratingData.movie_id.values),np.max(ratingData.user_id.values)),dtype=np.uint8)\n",
        "ratingMatrix[ratingData.movie_id.values-1, ratingData.user_id.values-1] = ratingData.rating.values\n",
        "print(ratingMatrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddncTYdF8ikR",
        "outputId": "38122e6d-e08c-40b3-9832-c5931e46229f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[240 151 243 ... 215   4   5]\n",
            " [  0  48 173 ...  51 215 127]\n",
            " [  0   0 176 ... 243  51 215]\n",
            " ...\n",
            " [  0   0   0 ...   0   0   0]\n",
            " [  0   0   0 ...   0   0   0]\n",
            " [  0   0   0 ...   0   0   0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Subtract Mean off - Normalization***"
      ],
      "metadata": {
        "id": "d90qJMDM-FCF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "normalizedMatrix = ratingMatrix - np.asarray([(np.mean(ratingMatrix, 1))]).T\n",
        "print(normalizedMatrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fVdRsQR1-JrU",
        "outputId": "6df3f248-b0b6-4bd2-9199-3c34d6292caf"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1.61157973e+02  7.21579732e+01  1.64157973e+02 ...  1.36157973e+02\n",
            "  -7.48420268e+01 -7.38420268e+01]\n",
            " [-1.04283159e+02 -5.62831595e+01  6.87168405e+01 ... -5.32831595e+01\n",
            "   1.10716841e+02  2.27168405e+01]\n",
            " [-1.15372578e+02 -1.15372578e+02  6.06274218e+01 ...  1.27627422e+02\n",
            "  -6.43725782e+01  9.96274218e+01]\n",
            " ...\n",
            " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00 ...  0.00000000e+00\n",
            "   0.00000000e+00  0.00000000e+00]\n",
            " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00 ...  0.00000000e+00\n",
            "   0.00000000e+00  0.00000000e+00]\n",
            " [-7.45156483e-03 -7.45156483e-03 -7.45156483e-03 ... -7.45156483e-03\n",
            "  -7.45156483e-03 -7.45156483e-03]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Computing SVD***"
      ],
      "metadata": {
        "id": "orUV20R6-1bn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "A = normalizedMatrix.T / np.sqrt(ratingMatrix.shape[0]-1)\n",
        "U, S, V = np.linalg.svd(A)"
      ],
      "metadata": {
        "id": "lpz2JD7A-4Mw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Calculating cosine similarity, sort by most similar and return the top N***"
      ],
      "metadata": {
        "id": "d9nEZ3HAAQkv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def similar(ratingData, movie_id, top_n):\n",
        "  index = movie_id-1 #movie starts from 1\n",
        "  movie_row = ratingData[index, :]\n",
        "  magnitude = np.sqrt(np.einsum('ij','ij->i', ratingData, ratingData)) #Einstein summation | traditional matrix\n",
        "  similarity = np.dot(movie_row, ratingData.T) / (magnitude[index]*magnitude)\n",
        "  sort_indexes = np.argsort(-similarity) #Perform an indirect sort along the given axis (Last axis)\n",
        "  return sort_indexes[:top_n]\n"
      ],
      "metadata": {
        "id": "YNB2yvk9AcMH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Select k principal components to represent the movies, a movie_id to find recommendations and print the top_n results***"
      ],
      "metadata": {
        "id": "G9vJqDU_mexy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "k = 50\n",
        "movie_id = 23\n",
        "top_n = 10"
      ],
      "metadata": {
        "id": "8zJBnSu-muvF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}