POI recommendation through cross-region collaborative filtering

Recommending points of interest (POIs) to a user according to the user’s current location and past check-in activities is the focus in this paper. Previously proposed probabilistic and topic model-based methods predict the POIs based on the distribution of the POIs visited in the past, assuming that the next POI for the user follows the same distribution. Such methods tend to recommend the POIs in the cities or regions that the user has visited before because only such cities or regions have observed ratings for the user. Thus, these works are not suitable for a user who travels to a new city or region where she has not checked-in any POI previously. To address this issue, we distinguish the user preferences on the content of POIs from the user preferences on the POIs themselves. The former is long term and is independent of where POIs are located, and the latter is short term and is constrained by the proximity of the location of the POI and the user’s current location. This distinction motivates a location-independent modeling of user’s content preferences of POIs, and a location-aware modeling of user’s location preferences of POIs. The final recommendation of POIs is derived by combining the predicted rating on content and the predicted rating on location of POI. We evaluate this method using the Yelp and Foursquare data sets. This approach has superiority over the state-of-the-art and works well in the “new city” situation in which the user has not rated any of the POIs in the current region.

[1]  Changsheng Xu,et al.  Probabilistic sequential POIs recommendation via check-in data , 2012, SIGSPATIAL/GIS.

[2]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[3]  Gennady L. Andrienko,et al.  Spatial Generalization and Aggregation of Massive Movement Data , 2011, IEEE Transactions on Visualization and Computer Graphics.

[4]  Markus Zanker,et al.  Multi-criteria Ratings for Recommender Systems: An Empirical Analysis in the Tourism Domain , 2012, EC-Web.

[5]  Michael R. Lyu,et al.  Where You Like to Go Next: Successive Point-of-Interest Recommendation , 2013, IJCAI.

[6]  Tomoharu Iwata,et al.  Geo topic model: joint modeling of user's activity area and interests for location recommendation , 2013, WSDM.

[7]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[8]  Kyumin Lee,et al.  Toward traffic-driven location-based web search , 2011, CIKM '11.

[9]  Yizhou Sun,et al.  LCARS: a location-content-aware recommender system , 2013, KDD.

[10]  Mao Ye,et al.  Exploiting geographical influence for collaborative point-of-interest recommendation , 2011, SIGIR.

[11]  Wei-Ying Ma,et al.  Recommending friends and locations based on individual location history , 2011, ACM Trans. Web.

[12]  Mohamed F. Mokbel,et al.  Location-based and preference-aware recommendation using sparse geo-social networking data , 2012, SIGSPATIAL/GIS.

[13]  Mao Ye,et al.  Location recommendation for location-based social networks , 2010, GIS '10.

[14]  Xing Xie,et al.  Collaborative location and activity recommendations with GPS history data , 2010, WWW '10.

[15]  Thomas Liebig,et al.  Using Data from Location Based Social Networks for Urban Activity Clustering , 2013, AGILE Conf..

[16]  Kenneth Wai-Ting Leung,et al.  CLR: a collaborative location recommendation framework based on co-clustering , 2011, SIGIR.

[17]  Hui Xiong,et al.  Learning geographical preferences for point-of-interest recommendation , 2013, KDD.

[18]  Michael R. Lyu,et al.  Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks , 2012, AAAI.

[19]  Martin Ester,et al.  Spatial topic modeling in online social media for location recommendation , 2013, RecSys.

[20]  Chong Wang,et al.  Collaborative topic modeling for recommending scientific articles , 2011, KDD.

[21]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[22]  Ke Wang,et al.  Are Features Equally Representative? A Feature-Centric Recommendation , 2015, AAAI.

[23]  Ke Wang,et al.  Latent Factor Transition for Dynamic Collaborative Filtering , 2014, SDM.

[24]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[25]  Roberto Turrin,et al.  Performance of recommender algorithms on top-n recommendation tasks , 2010, RecSys '10.

[26]  Xing Xie,et al.  Mining interesting locations and travel sequences from GPS trajectories , 2009, WWW '09.

[27]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[28]  Ahmed Eldawy,et al.  LARS: A Location-Aware Recommender System , 2012, 2012 IEEE 28th International Conference on Data Engineering.