Improving item recommendation based on social tag ranking

Authors:
Taiga Yoshida;Go Irie;Takashi Satou;Akira Kojima;Suguru Higashino
Affiliations:
NTT Cyber Solutions Laboratories, NTT Corporation, Yokosuka, Kanagawa, Japan;NTT Cyber Solutions Laboratories, NTT Corporation, Yokosuka, Kanagawa, Japan;NTT Cyber Solutions Laboratories, NTT Corporation, Yokosuka, Kanagawa, Japan;NTT Cyber Space Laboratories, NTT Corporation, Yokosuka, Kanagawa, Japan;NTT Cyber Solutions Laboratories, NTT Corporation, Yokosuka, Kanagawa, Japan
Venue:
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Year:
2012

Citing 11
Cited 1

Automatic text processing: the transformation, analysis, and retrieval of information by computer

Automatic text processing: the transformation, analysis, and retrieval of information by computer
Pointing the way: active collaborative filtering

CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A Framework for Collaborative, Content-Based and Demographic Filtering

Artificial Intelligence Review - Special issue on data mining on the Internet
Item-based collaborative filtering recommendation algorithms

Proceedings of the 10th international conference on World Wide Web
Modern Information Retrieval

Modern Information Retrieval
Evaluating collaborative filtering recommender systems

ACM Transactions on Information Systems (TOIS)
Usage patterns of collaborative tagging systems

Journal of Information Science
Flickr tag recommendation based on collective knowledge

Proceedings of the 17th international conference on World Wide Web
Tag ranking

Proceedings of the 18th international conference on World wide web
Evaluating similarity measures for emergent semantics of social tagging

Proceedings of the 18th international conference on World wide web
Empirical analysis of predictive algorithms for collaborative filtering

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

Tagging users based on Twitter lists

International Journal of Web Engineering and Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Content-based filtering is a popular framework for item recommendation. Typical methods determine items to be recommended by measuring the similarity between items based on the tags provided by users. However, because the usefulness of tags depends on the annotator's skills, vocabulary and feelings, many tags are irrelevant. This fact degrades the accuracy of simple content-based recommendation methods. To tackle this issue, this paper enhances content-based filtering by introducing the idea of tag ranking, a state-of-the-art framework that ranks tags according to their relevance levels. We conduct experiments on videos from a video-sharing site. The results show that tag ranking significantly improves item recommendation performance, despite its simplicity.