Multi-objective optimization based location and social network aware recommendation

Social networks, personal blog pages, on-line transaction web-sites, expertise web pages and location based social networks provide an attractive platform for millions of users to share opinions, comments, ratings, etc. Having this kind of diverse and comprehensive information leads to difficulties for users to reach the most appropriate and reliable conclusions. Recommendation systems form one of the solutions to deal with the information overload problem by providing personalized services. Using spatial, temporal and social information on recommender systems is a recent trend that increases the performance. Also, taking into account more than one criterion can improve the performance of the recommender systems. In this paper, a location and social network aware recommender system enhanced with multi objective filtering is proposed and described. The results show that the proposed method reaches high coverage while preserving precision. Besides, the proposed method is not affected by the range of ratings and provides persistent results in different settings.

[1]  Nikos Manouselis,et al.  Experimental Analysis of Design Choices in multiattribute Utility Collaborative Filtering , 2007, Int. J. Pattern Recognit. Artif. Intell..

[2]  Ahmed Eldawy,et al.  LARS*: An Efficient and Scalable Location-Aware Recommender System , 2014, IEEE Transactions on Knowledge and Data Engineering.

[3]  Mohammad Ali Abbasi,et al.  Trust-Aware Recommender Systems , 2014 .

[4]  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.

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

[6]  Alejandro Bellogín,et al.  An empirical comparison of social, collaborative filtering, and hybrid recommenders , 2013, TIST.

[7]  Gediminas Adomavicius,et al.  New Recommendation Techniques for Multicriteria Rating Systems , 2007, IEEE Intelligent Systems.

[8]  Nikolaos F. Matsatsinis,et al.  UTA-Rec: a recommender system based on multiple criteria analysis , 2008, RecSys '08.

[9]  Huan Liu,et al.  Exploring Social-Historical Ties on Location-Based Social Networks , 2012, ICWSM.

[10]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[11]  Gediminas Adomavicius,et al.  Multi-Criteria Recommender Systems , 2011, Recommender Systems Handbook.

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

[13]  Wei-Guang Teng,et al.  Incorporating Multi-Criteria Ratings in Recommendation Systems , 2007, 2007 IEEE International Conference on Information Reuse and Integration.

[14]  Nikos Manouselis,et al.  Analysis and Classification of Multi-Criteria Recommender Systems , 2007, World Wide Web.

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

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

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

[18]  Sung-Bae Cho,et al.  Location-Based Recommendation System Using Bayesian User's Preference Model in Mobile Devices , 2007, UIC.

[19]  Chi-Hung Chi,et al.  Enhancing tag-based collaborative filtering via integrated social networking information , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[20]  Kevin C. Almeroth,et al.  Social computing: an intersection of recommender systems, trust/reputation systems, and social networks , 2012, IEEE Network.

[21]  Chao Liu,et al.  Recommender systems with social regularization , 2011, WSDM '11.

[22]  Karl Aberer,et al.  SoCo: a social network aided context-aware recommender system , 2013, WWW.