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A hybrid recommender systems based on weighted tags

Liang, Huizhi, Xu, Yue, Li, Yuefeng, Nayak, Richi, & Shaw, Gavin (2010) A hybrid recommender systems based on weighted tags. In 10th SIAM International Conference on Data Mining (SDM2010), 29 April-1 May, 2011, Renaissance Columbus Downtwon Hotel, Columbus, Ohio. (Unpublished)

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Abstract

Social tags in web 2.0 are becoming another important information source to describe the content of items as well as to profile users’ topic preferences. However, as arbitrary words given by users, tags contains a lot of noise such as tag synonym and semantic ambiguity a large number personal tags that only used by one user, which brings challenges to effectively use tags to make item recommendations. To solve these problems, this paper proposes to use a set of related tags along with their weights to represent semantic meaning of each tag for each user individually. A hybrid recommendation generation approaches that based on the weighted tags are proposed. We have conducted experiments using the real world dataset obtained from Amazon.com. The experimental results show that the proposed approaches outperform the other state of the art approaches.

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ID Code: 41890
Item Type: Conference Paper
Keywords: Collaborative Filtering, social tags, user profiling, personalization, web 2.0, recommender systems
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 2011 The Authors
Deposited On: 06 Jun 2011 08:04
Last Modified: 12 Jun 2011 07:34

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