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Issue No. 03 - March (2014 vol. 26)
ISSN: 1041-4347
pp: 766-779
Yi Cai , South China University of Technology, Guangzhou
Ho-fung Leung , The Chinese University of Hong Kong, Hong Kong
Qing Li , City University of Hong Kong, Hong Kong
Huaqing Min , South China University of Technology, Guangzhou
Jie Tang , Tsinghua University, Beijing
Juanzi Li , Tsinghua University, Beijing
Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in 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 corated items of users, or common users of items, as in traditional CF). To the best of our knowledge, there has been no prior work on investigating CF recommendation by combining object typicality. TyCo outperforms many CF recommendation methods on recommendation accuracy (in terms of MAE) with an improvement of at least 6.35 percent in Movielens data set, especially with sparse training data (9.89 percent improvement on MAE) and has lower time cost than other CF methods. Further, it can obtain more accurate predictions with less number of big-error predictions.
Motion pictures, Prototypes, Vectors, Collaboration, Recommender systems, Educational institutions

Y. Cai, H. Leung, Q. Li, H. Min, J. Tang and J. Li, "Typicality-Based Collaborative Filtering Recommendation," in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 3, pp. 766-779, 2014.
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