1. 国家计算机网络与信息安全管理中心,北京 100029; 2. 中国科学院大学,北京 100049; 3. 中国科学院 信息工程研究所,北京 100093; 4. University of California,Santa Cruz,USA
A Collaborative Filtering Algorithm Combing Location Information
LU Xiao1 ,WANG Shuxin2 ,WANG Bin3,LU Kai4
1. National Computer Network and Information Security Administration Center, Beijing 100029,China; 2. University of Chinese Academy of Sciences,Beijing 100049,China; 3. Institute of Information Engineering, Chinese Academy of Sciences,Beijing 100093, China; 4. University of California,Santa Cruz,USA)
Abstract：Recommendation system based on users consumption data is playing an increasingly large application value in e-commerce, And in these data, businesses location information which can effectively reflect the users personal geographical preference, would make an important significance on recommender system. Existing work generally use only users review data as well as the distance between locations, which cannot reflect the relationships between different locations, not to mention that user preferences in different locations should has different weight. This paper proceed from the perspective of geographical area, and study the users preferences within the area, as well as the impact of different area partition methods on recommend models. Then we explore to incorporate recommender systems with geographical information effectively, including the locations global effects and users regional preferences, proposing recommendation models, such as LGE, LGN and LRSVD. Experimental evaluation on Yelp dataset demonstrates that our models can effectively improve the prediction results comparing to the traditional methods.
2016, Vol. 30 
