Comprehensive Movie Recommendation System
Abstract
A recommender system, also known as a recommendation system, is a type of information filtering system that attempts to forecast a user's rating or preference for an item. This article designs and implements a complete movie recommendation system prototype based on the Genre, Pearson Correlation Coefficient, Cosine Similarity, KNN-Based, Content-Based Filtering using TFIDF and SVD, Collaborative Filtering using TFIDF and SVD, Surprise Library based recommendation system technology. Apart from that in this paper, we present a novel idea that applies machine learning techniques to construct a cluster for the movie based on genres and then observes the inertia value number of clusters were defined. The constraints of the approaches discussed in this work have been described, as well as how one strategy overcomes the disadvantages of another. The whole work has been done on the dataset Movie Lens present at the group lens website which contains 100836 ratings and 3683 tag applications across 9742 movies. These data were created by 610 users between March 29, 1996, and September 24, 2018.
Cite
@article{arxiv.2112.12463,
title = {Comprehensive Movie Recommendation System},
author = {Hrisav Bhowmick and Ananda Chatterjee and Jaydip Sen},
journal= {arXiv preprint arXiv:2112.12463},
year = {2021}
}
Comments
The paper was presented in the 8th International Conference on Business Analytics and Intelligence (ICBAI'21), December 20-22, 2021, Bangalore, India. This is the pre=print of the published version that appears in the conference proceedings. It is eight pages long, and it consists of nine tables