Movie Recommender System: Collaborative Filtering Methods

Abhishek Kumar Rai; Pooja Khanna; Pawan Singh

doi:10.54060/a2zjournals.jmss.66

Authors

Abhishek Kumar Rai Department of Computer Science and Engineering, Amity University Uttar Pradesh, Lucknow, India
Pooja Khanna Department of Computer Science and Engineering, Amity University Uttar Pradesh, Lucknow, India
Pawan Singh Department of Computer Science and Engineering, Amity University Uttar Pradesh, Lucknow, India

DOI:

https://doi.org/10.54060/a2zjournals.jmss.66

Keywords:

collaborative filtering, Cosine Similarity, Mean-Squared Distance, mean absolute error

Abstract

In the current digital era, personalized movie recommendation systems have become essential tools for aiding users in navigating the vast array of available cinematic content. This paper offers an indepth examination of the development and implementation of a Python-based movie recommender system, integrating content-based and collaborative filtering methodologies. Utilizing a comprehensive dataset encompassing diverse movie attributes and audience feedback metrics, our research aims to construct a resilient recommendation engine capable of delivering customized movie suggestions to users. Through a meticulous analysis and evaluation of various similarity metrics, including Pearson Correlation Coefficient, Spearman Rank Correlation Coefficient, Mean-Squared Distance, and Cosine Similarity, we assess the system's effectiveness in generating personalized recommendations. Our findings provide valuable insights into the strengths and weaknesses of each technique, thereby guiding future research and enhancements in movie recommendation systems.

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