Enhanced Collaborative Filtering Algorithm for Movies  Recommendation using Big Five Personality Traits

Aminu Y. Suleiman; Roko Abubakar; Babangida A. Albaba

doi:10.55529/jipirs.36.12.23

Enhanced Collaborative Filtering Algorithm for Movies Recommendation using Big Five Personality Traits

https://doi.org/10.55529/jipirs.36.12.23

Authors

Aminu Y. Suleiman Dept.of Computer Science, Umaru Musa Yar Adua University, Katsina, Nigeria
Roko Abubakar Computer Science Unit, Usmanu Danfodiyo University, Sokoto, Nigeria
Babangida A. Albaba Dept.of Computer Science, Umaru Musa YarAdua University, Katsina, Nigeria

Keywords:

Recommender System, Collaborative Filtering, Cold Start Problem, Big Five Personality Traits, Genre, K Nearest Neighbor.

Abstract

Recommender System suffers from data sparsity and cold start problems which arises when there is no sufficient rating history for user who has recently log into the system and no proper recommendations can be made. This paper develops an Enhanced collaborative filtering algorithm for Movie recommender system Using genre and Big five Personality traits (EMUBP) as system’s input. The experimental result shows that the EMUBP system improved recommendation quality and accuracy by 8.33% compared with the existing state-of-the-art using precision, recall and MAE.

Published

2023-10-01

How to Cite

Aminu Y. Suleiman, Roko Abubakar, & Babangida A. Albaba. (2023). Enhanced Collaborative Filtering Algorithm for Movies Recommendation using Big Five Personality Traits . Journal of Image Processing and Intelligent Remote Sensing, 3(06), 12–23. https://doi.org/10.55529/jipirs.36.12.23