Recommendation based on object typicality

Authors:
Yi Cai;Ho-fung Leung;Qing Li;Jie Tang;Juanzi Li
Affiliations:
City University of Hong Kong, Hong Kong, China;The Chinese University of Hong Kong, Hong Kong, China;City University of Hong Kong, Hong Kong, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China
Venue:
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Year:
2010

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Abstract

Current recommendation methods are mainly classified into content-based, collaborative filtering and hybrid methods. These methods are based on similarity measurements among items or users. In this paper, we investigate recommendation systems from a new perspective based on object typicality and propose a novel typicality-based recommendation approach. Experiments show that our method outperforms compared methods on recommendation quality.