Place Recommendation from Check-in Spots on Location-Based Online Social Networks

With rapid growth of the GPS enabled mobile device, location-based online social network services become more and more popular, and allow their users to share life experiences with location information. In this paper, we considered a method for recommending places to a user based on spatial databases of location-based online social network services. We used a user-based collaborative filtering method to make a set of recommended places. In the proposed method, we calculate similarity of users' check-in activities not only their positions but also their semantics such as "shopping", "eating", "drinking", and so forth. We empirically evaluated our method in a real database and found that it outperforms the naive singular value decomposition collaborative filtering recommendation by comparing the prediction accuracy.

[1]  Nitya Narasimhan,et al.  Using location for personalized POI recommendations in mobile environments , 2006, International Symposium on Applications and the Internet (SAINT'06).

[2]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[3]  Chi-Yin Chow,et al.  Towards location-based social networking services , 2010, LBSN '10.

[4]  Chin-Wan Chung,et al.  A User Similarity Calculation Based on the Location for Social Network Services , 2011, DASFAA.

[5]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

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

[7]  Masanori Sugimoto,et al.  An Outdoor Recommendation System based on User Location History , 2005, ubiPCMM.

[8]  Guy Shani,et al.  A Survey of Accuracy Evaluation Metrics of Recommendation Tasks , 2009, J. Mach. Learn. Res..

[9]  Thad Starner,et al.  Using GPS to learn significant locations and predict movement across multiple users , 2003, Personal and Ubiquitous Computing.

[10]  David W. McDonald,et al.  Recommending collaboration with social networks: a comparative evaluation , 2003, CHI '03.

[11]  Thorsten Strufe,et al.  A recommendation system for spots in location-based online social networks , 2011, SNS '11.

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

[13]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[14]  Xing Xie,et al.  Learning travel recommendations from user-generated GPS traces , 2011, TIST.

[15]  Richard A. Harshman,et al.  Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..

[16]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[17]  Xing Xie,et al.  GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory , 2010, IEEE Data Eng. Bull..

[18]  Guanling Chen,et al.  Sharing location in online social networks , 2010, IEEE Network.