Home > Published Issues > 2024 > Volume 15, No. 1, 2024 >
JAIT 2024 Vol.15(1): 10-16
doi: 10.12720/jait.15.1.10-16

Approach of Item-Based Collaborative Filtering Recommendation Using Energy Distance

Tu Cam Thi Tran 1, Lan Phuong Phan 2, and Hiep Xuan Huynh 2,*
1. Faculty of Information Technology, Vinh Long University of Technology Education (VLUTE), Vinh Long Province, Vietnam
2. College of Information and Communication Technology, Can Tho University (CTU), Can Tho City, Vietnam
Email: tuttc@vlute.edu.vn (T.C.T.T.); pplan@ctu.edu.vn (L.P.P.); hxhiep@ctu.edu.vn (H.X.H.)
*Corresponding author

Manuscript received March 17, 2023; revised May 5, 2023; accepted July 14, 2023, published January 3, 2024.

Abstract—The current collaborative filtering recommendation method using energy distance only focuses on the relationship between the user and the user, between the user group and the user group. This method has not yet considered the relationship between the item and the item. In this article, we mainly focus on proposing an item-based collaborative filtering model using the energy distance. The proposed model is evaluated on two popular datasets Jester5k and MovieLens100k. Besides, the proposed model is also compared with two item-based collaborative filtering models using the Cosine and Pearson measures. The experimental results have shown that the proposed model is better than two compared models.
 
Keywords—item-based, energy distance, collaborative filtering, incompatibility matrix, recommendation system

Cite: Tu Cam Thi Tran, Lan Phuong Phan, and Hiep Xuan Huynh, "Approach of Item-Based Collaborative Filtering Recommendation Using Energy Distance," Journal of Advances in Information Technology, Vol. 15, No. 1, pp. 10-16, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.

PREVIOUS PAPER
A Machine Learning Approach for Stroke Differential Diagnosis by Blood Biomarkers
NEXT PAPER
A Combined Approach Based on Antlion Optimizer with Particle Swarm Optimization for Enhanced Localization Performance in Wireless Sensor Networks