An Adaptive User Preferences Elicitation Scheme for Location Recommendation  

作　　者：WANG Fan 

机构地区：[1]Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing 100876, China [2]School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

出　　处：《Chinese Journal of Electronics》2016年第5期943-949,共7页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.60872051);the Mutual Project of Beijing Municipal Education Commission of China

摘　　要：User preferences elicitation is a key issue of location recommendation. This paper proposes an adaptive user preferences elicitation scheme based on Collaborative filtering(CF) algorithm for location recommendation. In this scheme, user preferences are divided into user static preferences and user dynamic preferences. The former is estimated based on location category information and historical ratings. Meanwhile, the latter is evaluated based on geographical information and two-dimensional cloud model. The advantage of this method is that it not only considers the diversity of user preferences, but also can alleviate the data sparsity problem. In order to predict user preferences of new locations more precisely, the scheme integrates the similarity of user static preferences, user dynamic preferences and social ties into CF algorithm. Furthermore, the scheme is parallelized on the Hadoop platform for significant improvement in efficiency. Experimental results on Yelp dataset demonstrate the performance gains of the scheme.User preferences elicitation is a key issue of location recommendation. This paper proposes an adaptive user preferences elicitation scheme based on Collaborative filtering（CF） algorithm for location recommendation. In this scheme, user preferences are divided into user static preferences and user dynamic preferences. The former is estimated based on location category information and historical ratings. Meanwhile, the latter is evaluated based on geographical information and two-dimensional cloud model. The advantage of this method is that it not only considers the diversity of user preferences, but also can alleviate the data sparsity problem. In order to predict user preferences of new locations more precisely, the scheme integrates the similarity of user static preferences, user dynamic preferences and social ties into CF algorithm. Furthermore, the scheme is parallelized on the Hadoop platform for significant improvement in efficiency. Experimental results on Yelp dataset demonstrate the performance gains of the scheme.

关 键 词：Location recommendation Adaptive User static preferences User dynamic preferences 

分 类 号：TP273.2[自动化与计算机技术—检测技术与自动化装置] TD922.7[自动化与计算机技术—控制科学与工程]