Acquiring user information needs for recommender systems
Nadee, Wanvimol, Li, Yuefeng, & Xu, Yue (2013) Acquiring user information needs for recommender systems. In Raghaven, Vijay, Hu, Xiaolin, Liau, Churn-Jung, & Treur, Jan (Eds.) Proceedings of the 2013 IEEE/WIC/ACM International Conferences on Web Intelligence (WI) and Intelligent Agent Technology (IAT), IEEE, Atlanta, Georgia, pp. 5-8.
Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based filtering approach tries to recommend items that are similar to new users' profiles. The fundamental issues include how to profile new users, and how to deal with the over-specialization in content-based recommender systems. Indeed, the terms used to describe items can be formed as a concept hierarchy. Therefore, we aim to describe user profiles or information needs by using concepts vectors. This paper presents a new method to acquire user information needs, which allows new users to describe their preferences on a concept hierarchy rather than rating items. It also develops a new ranking function to recommend items to new users based on their information needs. The proposed approach is evaluated on Amazon book datasets. The experimental results demonstrate that the proposed approach can largely improve the effectiveness of recommender systems.
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|Item Type:||Conference Paper|
|Keywords:||Recommender systems, Content-based recommender system, Item taxonomic descriptors, Concept hierarchy, Items' popularity, Concept vector, User interest|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright © 2013 by The Institute of Electrical and Electronics Engineers, Inc.|
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|Deposited On:||04 Feb 2014 22:50|
|Last Modified:||06 Feb 2014 01:09|
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