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

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MOVIE RECOMMENDATION SYSTEM

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

REQUIREMENTS


  • !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


  • ratings.csv
userId, movieId, rating, timestamp
  • tags.csv
userId, movieId, tag, timestamp
  • movies.csv
movieId, title, year, genres
  • links.csv
movieId, imdbId, tmdbId

INFORMATIONS


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

readme için

  • 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:

readme2

MOVIE RECOMMENDATION SYSTEM OUTPUT


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.

ex_system_output

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