Towards Context-aware Social Recommendation via Individual Trust

Incorporating social network information and contexts to improve recommendation performance has been drawing considerable attention recently. However, a majority of existing social recommendation approaches suffer from the following problems: (1) They only employ individual trust among users to optimize prediction solutions in user latent feature space or in user-item rating space; thus, they exhibit low recommendation accuracy. (2) They use decision trees to perform context-based user-item subgrouping; thus, they can only handle categorical contexts. (3) They have difficulty coping with the data sparsity problem. To solve these problems, and accurately and realistically model recommender systems, we propose a social matrix factorization method to optimize the prediction solution in both user latent feature space and user-item rating space using the individual trust among users. To further improve the recommendation performance and alleviate the data sparsity problem, we propose a context-aware enhanced model based on Gaussian mixture model (GMM). Two real datasets (one sparse dataset and one dense dataset) based experiments show that our proposed method outperforms the state-of-the-art social matrix factorization and context-aware recommendation methods in terms of prediction accuracy.

[1]  P. Dirac.  Principles of Quantum Mechanics , 1982 .

[2]  Zhen Lin,et al.  Context-Aware Collaborative Topic Regression with Social Matrix Factorization for Recommender Systems , 2014, AAAI.

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

[4]  Irwin King,et al.  An Enhanced Semi-supervised Recommendation Model Based on Green's Function , 2010, ICONIP.

[5]  Jing Liu,et al.  Domain-Sensitive Recommendation with User-Item Subgroup Analysis , 2016, IEEE Transactions on Knowledge and Data Engineering.

[6]  Bernhard Schölkopf,et al.  Introduction to Semi-Supervised Learning , 2006, Semi-Supervised Learning.

[7]  Yan Wang,et al.  Capturing Semantic Correlation for Item Recommendation in Tagging Systems , 2016, AAAI.

[8]  Jurij F. Tasic,et al.  Relevant Context in a Movie Recommender System: Users' Opinion vs. Statistical Detection , 2012 .

[9]  Francesco Ricci,et al.  Context-Aware Recommender Systems , 2011, AI Mag..

[10]  Zoubin Ghahramani,et al.  Learning from labeled and unlabeled data with label propagation , 2002 .

[11]  Min Zhao,et al.  Adapting neighborhood and matrix factorization models for context aware recommendation , 2010, RecSys 2010.

[12]  Chris H. Q. Ding,et al.  Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs , 2010, SDM.

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

[14]  Deren Chen,et al.  Recommender System Based on Social Trust Relationships , 2013, 2013 IEEE 10th International Conference on e-Business Engineering.

[15]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[16]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[17]  Yan Liu,et al.  Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems , 2012, ICML.

[18]  James A. Hendler,et al.  Accuracy of Metrics for Inferring Trust and Reputation in Semantic Web-Based Social Networks , 2004, EKAW.

[19]  Vanja Josifovski,et al.  Focused matrix factorization for audience selection in display advertising , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[20]  Chun Chen,et al.  An exploration of improving collaborative recommender systems via user-item subgroups , 2012, WWW.

[21]  Steffen Rendle,et al.  Factorization Machines , 2010, 2010 IEEE International Conference on Data Mining.

[22]  Hanghang Tong,et al.  RaPare: A Generic Strategy for Cold-Start Rating Prediction Problem , 2017, IEEE Transactions on Knowledge and Data Engineering.

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

[24]  Kevin Chen-Chuan Chang,et al.  Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically , 2014, ICML.

[25]  Alexander Tuzhilin,et al.  Using Context to Improve Predictive Modeling of Customers in Personalization Applications , 2008, IEEE Transactions on Knowledge and Data Engineering.

[26]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[27]  Shunxiang Wu,et al.  Version-sensitive mobile App recommendation , 2017, Inf. Sci..

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

[29]  Paolo Avesani,et al.  A trust-enhanced recommender system application: Moleskiing , 2005, SAC '05.

[30]  Xin Liu.  Towards Context-Aware Social Recommendation via Trust Networks , 2013, WISE.

[31]  Zhen Lin,et al.  A Hybrid Trust-Based Recommender System for Online Communities of Practice , 2015, IEEE Transactions on Learning Technologies.

[32]  S. Lauritzen,et al.  Chain graph models and their causal interpretations , 2002 .

[33]  M. Seeger.  Learning with labeled and unlabeled dataMatthias , 2001 .

[34]  Chris H. Q. Ding,et al.  A learning framework using Green's function and kernel regularization with application to recommender system , 2007, KDD '07.

[35]  Bernd Ludwig,et al.  Matrix factorization techniques for context aware recommendation , 2011, RecSys '11.

[36]  Wei Wang,et al.  Recommender system application developments: A survey , 2015, Decis. Support Syst..

[37]  Ya Zhang,et al.  Collaborative filtering with social regularization for TV program recommendation , 2013, Knowl. Based Syst..

[38]  Jie Lu,et al.  Multirelational Social Recommendations via Multigraph Ranking , 2017, IEEE Transactions on Cybernetics.

[39]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

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

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

[42]  Fernando Ortega,et al.  A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model , 2016, Knowl. Based Syst..

[43]  Seyoung Kim,et al.  On Sparse Gaussian Chain Graph Models , 2014, NIPS.

[44]  Lars Schmidt-Thieme,et al.  Fast context-aware recommendations with factorization machines , 2011, SIGIR.

[45]  Deepak Agarwal,et al.  fLDA: matrix factorization through latent dirichlet allocation , 2010, WSDM '10.

[46]  Alda Lopes Gançarski,et al.  A Contextual-Bandit Algorithm for Mobile Context-Aware Recommender System , 2012, ICONIP.

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

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

[49]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[50]  Qiang Yang,et al.  Contextual Collaborative Filtering via Hierarchical Matrix Factorization , 2012, SDM.

[51]  Deepak Agarwal,et al.  Regression-based latent factor models , 2009, KDD.

[52]  Lior Rokach,et al.  Towards latent context-aware recommendation systems , 2016, Knowl. Based Syst..

[53]  Jure Leskovec,et al.  Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.

[54]  Lester W. Mackey.  Latent Dirichlet Markov Random Fields for Semi-supervised Image Segmentation and Object Recognition , 2007 .

[55]  Domonkos Tikk,et al.  Investigation of Various Matrix Factorization Methods for Large Recommender Systems , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[56]  Fei Wang,et al.  Recommendation on Item Graphs , 2006, Sixth International Conference on Data Mining (ICDM'06).

[57]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[58]  Lars Schmidt-Thieme,et al.  Online-updating regularized kernel matrix factorization models for large-scale recommender systems , 2008, RecSys '08.

[59]  Tsvi Kuflik,et al.  Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011) , 2011, RecSys '11.

[60]  Martha Larson,et al.  Collaborative Filtering beyond the User-Item Matrix , 2014, ACM Comput. Surv..

[61]  Douglas A. Reynolds,et al.  Gaussian Mixture Models , 2018, Encyclopedia of Biometrics.

[62]  Jason J. Jung,et al.  Exploiting matrix factorization to asymmetric user similarities in recommendation systems , 2015, Knowl. Based Syst..

[63]  Xi Zhang,et al.  How friends affect user behaviors? An exploration of social relation analysis for recommendation , 2015, Knowl. Based Syst..

[64]  Paolo Avesani,et al.  Trust-Aware Collaborative Filtering for Recommender Systems , 2004, CoopIS/DOA/ODBASE.

[65]  Deren Chen,et al.  Context-Aware Trust Aided Recommendation via Ontology and Gaussian Mixture Model in Big Data Environment , 2014, 2014 International Conference on Service Sciences.

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