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 ...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.展开更多
Dear Editor,For virologists,it is crucial to confidently determine the concentration of infectious particles that are utilized and produced in experiments（Dulbecco,1952;Bushar and Sagripanti,1990;La Barre and Lowy,20...Dear Editor,For virologists,it is crucial to confidently determine the concentration of infectious particles that are utilized and produced in experiments（Dulbecco,1952;Bushar and Sagripanti,1990;La Barre and Lowy,2001;Gueret et al.,2002;Gao et al.,2009;Kutner et al.,2009;Grigorov et al.,2011.展开更多
基金supported by National Key Basic Research Program of China(973 Program) under Grant No.2014CB340404National Natural Science Foundation of China under Grant Nos.61272111 and 61273216Youth Chenguang Project of Science and Technology of Wuhan City under Grant No. 2014070404010232
文摘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.
基金supported in part by NIH Research Grant R01NS081109 to SVHthe content is solely the responsibility of the authors and does not necessarily represent the official views of the NINDS/NIHappreciate the assistance of the editorial staff at UMES
文摘Dear Editor,For virologists,it is crucial to confidently determine the concentration of infectious particles that are utilized and produced in experiments（Dulbecco,1952;Bushar and Sagripanti,1990;La Barre and Lowy,2001;Gueret et al.,2002;Gao et al.,2009;Kutner et al.,2009;Grigorov et al.,2011.
