Improving Recommendation Novelty Based on Topic Taxonomy

Weng, Li-Tung, Xu, Yue, Li, Yuefeng, & Nayak, Richi (2007) Improving Recommendation Novelty Based on Topic Taxonomy. In 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, 2-5 November 2007, Silicon Valley.

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Clustering has been a widely applied approach to improve the computation efficiency of collaborative filtering based recommendation systems. Many techniques have been suggested to discover the item-to-item, user-to- user, and item-to-user associations within user clusters. However, there are few systems utilize the cluster based topic-to-topic associations to make recommendations. This paper suggests a taxonomy-based recommender system that utilizes cluster based topic-to-topic associations to improve its recommendation quality and novelty.

Impact and interest:

17 citations in Scopus
6 citations in Web of Science®
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ID Code: 14309
Item Type: Conference Paper
Refereed: Yes
Keywords: information filtering, pattern clustering
DOI: 10.1109/WI-IATW.2007.59
ISBN: 0769530281
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2007 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 06 Aug 2008 00:00
Last Modified: 29 Feb 2012 13:34

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