Point-of-Interest Recommendation in LocationBased Social Networks with Personalized Geo-Social Influence  

作　　者：HUANG Liwei MA Yutao LIU Yanbo 

机构地区：[1]Beijing Institute of Remote Sensing [2]School of Computer,Wuhan University [3]WISET Automation Company Limited,Wuhan Iron and Steel Group Corporation

出　　处：《China Communications》2015年第12期21-31,共11页中国通信（英文版）

基　　金：supported by National Key Basic Research Program of China(973 Program) under Grant No.2014CB340404;National Natural Science Foundation of China under Grant Nos.61272111 and 61273216;Youth Chenguang Project of Science and Technology of Wuhan City under Grant No. 2014070404010232

摘　　要：Point-of-interest(POI) recommendation is a popular topic on location-based social networks(LBSNs).Geographical proximity,known as a unique feature of LBSNs,significantly affects user check-in behavior.However,most of prior studies characterize the geographical influence based on a universal or personalized distribution of geographic distance,leading to unsatisfactory recommendation results.In this paper,the personalized geographical influence in a two-dimensional geographical space is modeled using the data field method,and we propose a semi-supervised probabilistic model based on a factor graph model to integrate different factors such as the geographical influence.Moreover,a distributed learning algorithm is used to scale up our method to large-scale data sets.Experimental results based on the data sets from Foursquare and Gowalla show that our method outperforms other competing POI recommendation techniques.Point-of-interest（POI） recommendation is a popular topic on location-based social networks（LBSNs）.Geographical proximity,known as a unique feature of LBSNs,significantly affects user check-in behavior.However,most of prior studies characterize the geographical influence based on a universal or personalized distribution of geographic distance,leading to unsatisfactory recommendation results.In this paper,the personalized geographical influence in a two-dimensional geographical space is modeled using the data field method,and we propose a semi-supervised probabilistic model based on a factor graph model to integrate different factors such as the geographical influence.Moreover,a distributed learning algorithm is used to scale up our method to large-scale data sets.Experimental results based on the data sets from Foursquare and Gowalla show that our method outperforms other competing POI recommendation techniques.

关 键 词：probabilistic geographical integrate prior modeled supervised utilized Recommendation automatically iteration 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]