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TyCo: Towards Typicality-based Collaborative Filtering Recommendation

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5 Author(s)
Yi Cai ; Dept. of Comput. Sci., City Univ. of Hong Kong, Hong Kong, China ; Ho-fung Leung ; Li, Qing ; Jie Tang
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Collaborative filtering (CF) is an important and popular technology for recommendation systems. However, current collaborative filtering methods suffer from some problems such as sparsity problem, inaccurate recommendation and producing big-error predictions. In this paper, we borrow ideas of object typicality from cognitive psychology and propose a novel typicality-based collaborative filtering recommendation method named TyCo. A distinct feature of typicality-based CF is that it finds `neighbors' of users based on user typicality degrees in user groups (instead of the co-rated items of users or common users of items in traditional CF). To the best of our knowledge, there is no work on investigating collaborative filtering recommendation by combining object typicality. We conduct experiments to validate TyCo and compare it with previous methods.

Published in:

Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on  (Volume:2 )

Date of Conference:

27-29 Oct. 2010

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