Movie Recommendation System

Tejas Gupta      Abhinav Gupta      Maulik Desai      Harshit Yadav      Mohit Kumar      Samay Mehar

 


Code Report Streamlit

Abstract

In the contemporary landscape of digital media consumption, the vast array of available movies presents a challenge for users seeking personalized recommendations. In response, this project introduces a machine learning- based movie recommendation system designed to address this challenge. Our system leverages user preferences and historical movie ratings to offer tailored recommendations, thereby enhancing user engagement and satisfaction with movie platforms. We employ collaborative filtering, content-based filtering, and matrix factorization techniques to generate accurate and diverse movie suggestions. Additionally, we conduct thorough performance evaluations us- ing various metrics and compare our system with existing recommendation approaches. The results demonstrate the efficacy and efficiency of our method in delivering relevant movie recommendations, with implications for enhanc- ing user satisfaction and engagement in movie consumption platforms.

Video

Visual Comparisons

 

References

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