Home > Table of Contents

Proceedings of the 2nd International Symposium on Web Information Systems and Applications (WISA 2009)

Nanchang, China, May 22-24, 2009

Editors: Fei Yu, Jiexian Zeng, and Guangxue Yue

AP Catalog Number: AP-PROC-CS-09CN001

ISBN: 978-952-5726-00-8 (Print), 978-952-5726-01-5 (CD-ROM)

Page(s): 29-34

Improved Memory-based Collaborative Filtering Using Entropy-based Similarity Measures

           Hyeong-Joon Kwon, Tae-Hoon Lee, Kwang-Seok Hong

Full text:  PDF

Abstract

Accuracy of predicting the user preference score is the most important element of collaborative filtering. This paper proposes novel similarity measures using difference score entropy of common rating items between two users. The proposed similarity measures can apply various weights according to the score difference, to evaluate the similarity. We implemented a recommender system using the proposed similarity measures and, experimented on performance with memory-based collaborative filtering. Based on the experimental results, the proposed similarity measures significantly improve the prediction accuracy with respect to existing similarity measures, and we confirmed that the proposed measure is robust to sparse data sets.

Index Terms

similarity, collaborative filtering, entropy

Copyright @ 2009 ACADEMY PUBLISHER — All rights reserved