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

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

DOI:

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

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.

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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

Issue

Vol. 3 No. 06 (2023): Oct-Nov 2023

Section

Articles

License

Copyright (c) 2023 Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.