Finding and Applying Typical User Group in Recommender Systems
TAN Chang1, LIU Qi1, WU Le1, MA Hai-Ping2, LONG Bo3
1.School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027 2.iFLYTEK Co.,Ltd., Hefei 230088. 3.China Electronic Technology Group Corporation No.38 Research Institute, Hefei 230088
Abstract：Recommender system (RS) provides an effective way to solve the personalized information needs of users. However, with the expansion of the user scale, it is necessary to find some subsets of vast amounts of RS users, and the continuous and in-depth analysis for these user subsets can be used to improve the RS. Therefore, the typical user group (TUG) is defined as a representative subset of the entire users in RS to correctly reflect the preferences of all the users. Then, a weighted typical user group finding algorithm (WTFA) is designed to compare the contributions of the candidate typical users and choose the typical users with higher contribution, so that a TUG is built with high item coverage rate and rating accuracy. A modified TUG-based collaborative filtering(TUG-CF)algorithm is developed to discover the nearest neighbors in TUG. The experimental results on real world dataset show that TUG is better than most rating user group and maximizes diversified user group on item coverage rate and rating accuracy, and TUG-CF has better recommendation results than traditional collaborative filtering methods.
谭昶，刘淇，吴乐，马海平，龙柏. 推荐系统中典型用户群组的发现和应用*[J]. 模式识别与人工智能, 2015, 28(5): 462-471.
TAN Chang, LIU Qi, WU Le, MA Hai-Ping, LONG Bo. Finding and Applying Typical User Group in Recommender Systems. , 2015, 28(5): 462-471.
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