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Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on (2013)
Atlanta, GA, USA USA
Nov. 17, 2013 to Nov. 20, 2013
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.
user interest, Recommender systems, content-based recommender system, item taxonomic descriptors, concept hierarchy, items' popularity, concept vector

W. Nadee, Y. Li and Y. Xu, "Acquiring User Information Needs for Recommender Systems," 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)(WI-IAT), Atlanta, GA, USA, 2013, pp. 5-8.
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