Improving recommendation based on features' co-occurrence effects in collaborative tagging systems
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
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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.