A Novel Recommendation Model Regularized with User Trust and Item Ratings

We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques.

[1]  Martin Ester,et al.  A generalized stochastic block model for recommendation in social rating networks , 2011, RecSys '11.

[2]  Jiming Liu,et al.  Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Social Collaborative Filtering by Trust , 2022 .

[3]  Mehrnoush Shamsfard,et al.  Matrix Factorization with Explicit Trust and Distrust Side Information for Improved Social Recommendation , 2014, TOIS.

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

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

[6]  Tong Zhao,et al.  Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering , 2014, CIKM.

[7]  Hareton K. N. Leung,et al.  Improving network topology-based protein interactome mapping via collaborative filtering , 2015, Knowl. Based Syst..

[8]  Yehuda Koren,et al.  Factor in the neighbors: Scalable and accurate collaborative filtering , 2010, TKDD.

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

[10]  Chun Chen,et al.  Social Recommendation Using Low-Rank Semidefinite Program , 2011, AAAI.

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

[12]  Nicola Barbieri,et al.  Probabilistic Approaches to Recommendations , 2014, Probabilistic Approaches to Recommendations.

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

[14]  Guibing Guo,et al.  Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems , 2013, RecSys.

[15]  Hao Ma.  On measuring social friend interest similarities in recommender systems , 2014, SIGIR.

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

[17]  Yang Guo,et al.  On top-k recommendation using social networks , 2012, RecSys.

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

[19]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[20]  Congfu Xu,et al.  Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks , 2015, Knowl. Based Syst..

[21]  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.

[22]  Jie Zhang,et al.  Leveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation , 2014, AAAI.

[23]  Yanchun Zhang,et al.  Modeling dual role preferences for trust-aware recommendation , 2014, SIGIR.

[24]  Neil Yorke-Smith,et al.  TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings , 2015, AAAI.

[25]  Li Chen,et al.  Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation , 2011, RecSys '11.

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

[27]  Jun Zhang,et al.  Single-trial ERPs denoising via collaborative filtering on ERPs images , 2015, Neurocomputing.

[28]  Harald Steck,et al.  Circle-based recommendation in online social networks , 2012, KDD.

[29]  Rino Falcone,et al.  Trust Theory: A Socio-Cognitive and Computational Model , 2010 .

[30]  Daniel Thalmann,et al.  A simple but effective method to incorporate trusted neighbors in recommender systems , 2012, UMAP.

[31]  Sung Jin Hur,et al.  Improved trust-aware recommender system using small-worldness of trust networks , 2010, Knowl. Based Syst..

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

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

[34]  Martha Larson,et al.  Nontrivial landmark recommendation using geotagged photos , 2013, TIST.

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

[36]  Neil Yorke-Smith,et al.  Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems , 2015, Knowl. Based Syst..