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Connecting users and items with weighted tags for personalized item recommendations

Liang, Huizhi ; Xu, Yue ; Li, Yuefeng ; Nayak, Richi ; Tao, Xiaohui

Proceedings of the 21st ACM conference on Hypertext and hypermedia, 2010, p.51-60

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Título:
Connecting users and items with weighted tags for personalized item recommendations
Autor: Liang, Huizhi ; Xu, Yue ; Li, Yuefeng ; Nayak, Richi ; Tao, Xiaohui
Assuntos: Human-centered computing -- Collaborative and social computing ; Information systems -- Information retrieval
É parte de: Proceedings of the 21st ACM conference on Hypertext and hypermedia, 2010, p.51-60
Descrição: 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.
Editor: New York, NY, USA: ACM
Idioma: Inglês

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