Personalized recommender systems integrating tags and item taxonomy

Article type: Research Article

Authors: Liang, Huizhi^; | Xu, Yue | Li, Yuefeng

Affiliations: School of Information Technology, Queensland University of Technology, Brisbane, Australia. E-mail: [email protected], {yue.xu,y2.li}@qut.edu.au

Note: [] Corresponding author.

Abstract: Tags in Web 2.0 are becoming another important information source to profile users' interests and preferences to make personalized recommendations. To solve the problem of low information sharing caused by the free-style vocabulary of tags and the long tails of the distribution of tags and items, this paper proposes an approach to integrate the social tags given by users and the item taxonomy with standard vocabulary and hierarchical structure provided by experts to make personalized recommendations. The experimental results show that the proposed approach can effectively improve the information sharing and recommendation accuracy.

Keywords: Recommender systems, tags, user profiling, personalization, Web 2.0, taxonomy

DOI: 10.3233/WIA-2012-0246

Journal: Web Intelligence and Agent Systems: An International Journal, vol. 10, no. 3, pp. 277-289, 2012