2025
doi: 10.12720/jait.16.7.1042-1047
Item-Based Context-Aware Collaborative Filtering Using Energy Distance with Pre-filtering Contextual Feature
Linh Thuy Thi Nguyen1,2
,
Lan Phuong Phan1 ,
Hiep Xuan Huynh1,2,*
- 1College of Information and Communication Technology, Can Tho University, Can Tho City, Vietnam
- 2CTU Leading Research Team on Automation, Artificial Intelligence, Information Technology and Digital Transformation (CTU-AIMED), Can Tho University, Can Tho, Vietnam
- Manuscript receivedJanuary 24, 2025
- revisedFebruary 21, 2025
- acceptedApril 23, 2025
- publishedJuly 25, 2025
Abstract
This paper proposes an Item-Based Context-Aware Collaborative Filtering (IB-CACF) approach that combines Energy Distance and Pre-Filtering Contextual Features to improve the effectiveness of recommendation systems. The proposed method addresses the limitations of traditional recommendation systems, which fail to consider contextual factors such as time, location, and companions. Energy Distance measures the similarity between items in the context space, enhancing prediction accuracy. Along with Pre-Filtering Contextual Features, this approach reduces computational cost by filtering out the most relevant contextual factors before similarity calculations. Experimental results on the MovieLens 25M, Amazon, and Yelp datasets demonstrate that this method outperforms existing approaches in terms of both prediction accuracy and computational efficiency.
Keywords
- item-based
- energy distance
- context-aware
- collaborative filtering
- contextual pre-filtering
How to Cite
Linh Thuy Thi Nguyen, Lan Phuong Phan, and Hiep Xuan Huynh, "Item-Based Context-Aware Collaborative Filtering Using Energy Distance with Pre-filtering Contextual Feature," Journal of Advances in Information Technology, vol. 16, no. 7, pp. 1042-1047, 2025. https://doi.org/10.12720/jait.16.7.1042-1047
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
