Integrating AI for Enhanced Exploration of Video Recommendation Algorithm via Improved Collaborative Filtering

Authors

  • Yafei Xiang Northeastern University, Computer Science, Boston, MA, USA
  • Shuning Huo Virginia Tech, Statistics, Blacksburg, VA, USA
  • Yichao Wu Northeastern University, Computer Science, Boston, MA, USA
  • Yulu Gong Northern Arizona University, Computer & Information Technology, Flagstaff, AZ, USA
  • Mengran Zhu Miami University, Computer Engineering, Oxford, OH, USA

DOI:

https://doi.org/10.53469/jtpes.2024.04(02).12

Keywords:

Artificial Intelligence, Video Recommendation, Collaborative Filtering, User Interest

Abstract

This study tackles the issue of poor user experience in video recommendation systems, primarily caused by the scarcity of user ratings and the inaccuracy of existing recommendation methods. We propose a novel algorithm that leverages Artificial Intelligence (AI) to better align with user interests and video metadata. Our approach begins by analyzing specific user behavior data to transition from traditional item rating matrices to more representative user interest matrices. We then enhance video tag analysis by incorporating a weighting factor, facilitating more precise video similarity calculations and the identification of similar video suggestions. The algorithm ultimately recommends a Top-N selection of videos to users. Through rigorous testing, including a comparison against the Movie-Lens dataset, our results exhibit a 15% increase in recommendation accuracy, underscoring the efficiency of our AI-powered method.

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Published

2024-03-01

How to Cite

Xiang, Y., Huo, S., Wu, Y., Gong, Y., & Zhu, M. (2024). Integrating AI for Enhanced Exploration of Video Recommendation Algorithm via Improved Collaborative Filtering. Journal of Theory and Practice of Engineering Science, 4(02), 83–90. https://doi.org/10.53469/jtpes.2024.04(02).12

Issue

Vol. 4 No. 02 (2024): JTPES-2023-4-2

Section

Articles

License

Copyright (c) 2024 Yafei Xiang, Shuning Huo, Yichao Wu, Yulu Gong, Mengran Zhu

Creative Commons License

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