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Connecting users and items with weighted tags for personalized item recommendations

Liang, Huizhi, Xu, Yue, Li, Yuefeng, & Nayak, Richi (2010) Connecting users and items with weighted tags for personalized item recommendations. In Proceedings of 21st ACM Conference on HyperText and HyperMedia, ACM, Toronto.

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Abstract

Social tags are an important information source in Web 2.0. They can be used to describe users’ topic preferences as well as the content of items to make personalized recommendations. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. To eliminate the noise of tags, in this paper we propose to use the multiple relationships among users, items and tags to find the semantic meaning of each tag for each user individually. With the proposed approach, the relevant tags of each item and the tag preferences of each user are determined. In addition, the user and item-based collaborative filtering combined with the content filtering approach are explored. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on real world datasets collected from Amazon.com and citeULike website.

Impact and interest:

16 citations in Scopus
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ID Code: 41887
Item Type: Conference Paper
Keywords: Recommender systems, Tags, Personalization, Web 2.0
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2010 ACM
Deposited On: 06 Jun 2011 14:20
Last Modified: 12 Jun 2011 07:33

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