DMFA-SR: Deeper Membership and Friendship Awareness for Social Recommendation

The existing social recommendation models mostly utilize various explicit user-generated information. Although there exist a few studies adopting the implicit relationship between users for social recommendation, however, these studies do not consider the deeper social relationship, nor simultaneously take into account two or more deeper relationships between users from different angles. To this end, we propose a new deeper membership and friendship awareness for social recommendation. Specifically, we first calculate the deeper membership similarity between users utilizing the improved Jaccard similarity coefficient and the deeper friendship similarity between users using the proposed two-hop random walk algorithm. Second, the deeper membership similarity and the deeper friendship similarity are combined in a unified way to form a comprehensive deeper social relation similarity. Third, we adopt the matrix factorization method incorporating the deeper membership and the deeper friendship between users as a regularization term for social recommendation, and the corresponding comprehensive deeper social relationship similarity is regarded as the regularization parameter. Experiments on two real-world datasets demonstrate the superiority of the proposed recommendation model.

[1]  Jennifer Golbeck,et al.  Generating Predictive Movie Recommendations from Trust in Social Networks , 2006, iTrust.

[2]  Martin Ester,et al.  A matrix factorization technique with trust propagation for recommendation in social networks , 2010, RecSys '10.

[3]  Michael R. Lyu,et al.  Introduction to social recommendation , 2010, WWW '10.

[4]  Jun Wang,et al.  Unifying user-based and item-based collaborative filtering approaches by similarity fusion , 2006, SIGIR.

[5]  Huan Liu,et al.  Social recommendation: a review , 2013, Social Network Analysis and Mining.

[6]  Bamshad Mobasher,et al.  Model-Based Collaborative Filtering as a Defense against Profile Injection Attacks , 2006, AAAI.

[7]  Chengqi Zhang,et al.  Global and Local Influence-based Social Recommendation , 2016, CIKM.

[8]  Xindong Wu,et al.  Positive and Unlabeled Multi-Graph Learning , 2017, IEEE Transactions on Cybernetics.

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

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

[11]  Philip S. Yu,et al.  Bag Constrained Structure Pattern Mining for Multi-Graph Classification , 2014, IEEE Transactions on Knowledge and Data Engineering.

[12]  Michael R. Lyu,et al.  Learning to recommend with social trust ensemble , 2009, SIGIR.

[13]  Ido Guy,et al.  Social Recommender Systems , 2015, Recommender Systems Handbook.

[14]  J. Onyx,et al.  Measuring Social Capital in Five Communities , 2000 .

[15]  Martin Ester,et al.  TrustWalker: a random walk model for combining trust-based and item-based recommendation , 2009, KDD.

[16]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[17]  Fei Wang,et al.  Social contextual recommendation , 2012, CIKM.

[18]  Huan Liu,et al.  Exploiting Local and Global Social Context for Recommendation , 2013, IJCAI.

[19]  Hui Tian,et al.  A new user similarity model to improve the accuracy of collaborative filtering , 2014, Knowl. Based Syst..

[20]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[21]  Juan-Zi Li,et al.  Typicality-Based Collaborative Filtering Recommendation , 2014, IEEE Transactions on Knowledge and Data Engineering.

[22]  Dongqing Xie,et al.  Social Influence Analysis in Social Networking Big Data: Opportunities and Challenges , 2017, IEEE Network.

[23]  Kamal Kant Bharadwaj,et al.  A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity , 2012, Social Network Analysis and Mining.

[24]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[25]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[26]  Huan Liu,et al.  gSCorr: modeling geo-social correlations for new check-ins on location-based social networks , 2012, CIKM.

[27]  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.

[28]  Michael R. Lyu,et al.  SoRec: social recommendation using probabilistic matrix factorization , 2008, CIKM '08.

[29]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[30]  Scott Sanner,et al.  New objective functions for social collaborative filtering , 2012, WWW.

[31]  Huan Liu,et al.  eTrust: understanding trust evolution in an online world , 2012, KDD.

[32]  Le Yu,et al.  Adaptive social similarities for recommender systems , 2011, RecSys '11.

[33]  Zhihua Cai,et al.  Boosting for Multi-Graph Classification , 2015, IEEE Transactions on Cybernetics.

[34]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[35]  Peng Zhang,et al.  SODE: Self-Adaptive One-Dependence Estimators for classification , 2016, Pattern Recognit..

[36]  Chris Cornelis,et al.  Trust and Recommendations , 2011, Recommender Systems Handbook.