MOVIE RECOMMENDATION SYSTEM

Movie Recommendation System using PySpark, ALS, SQLLite (Movielens Dataset)

REQUIREMENTS

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• !pip install sqlite3
• !pip install pyspark

All of the required modules and libraries are listed below:

• for machine learning processes

from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.recommendation import ALS
from pyspark.ml.tuning import TrainValidationSplit, ParamGridBuilder

• for dataframe and spark session processes

from pyspark.sql import SparkSession
from pyspark.sql.functions import * 
from pyspark.sql.types import *
from pyspark.context import SparkContext
from datetime import date, timedelta, datetime
import time

MOVIELENS DATASET INFORMATION

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• ratings.csv

userId, movieId, rating, timestamp

• tags.csv

userId, movieId, tag, timestamp

• movies.csv

movieId, title, year, genres

• links.csv

movieId, imdbId, tmdbId

INFORMATIONS

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• The file hierarchy required for the code to work properly is as shown in the figure below:

• All the necessary directions for the code are explained in detail in the code, and the code is divided into certain sections as shown below according to its function:

MOVIE RECOMMENDATION SYSTEM OUTPUT

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Movie recommendation system that filters the User_Id information according to the user we want and lists the most compatible movies for that user.

Please edit the filter .filter("User_Id = N") with the user's ID (N) according to the user you want.