Recommendations in Signed Social Networks

Recommender systems play a crucial role in mitigating the information overload problem in social media by suggesting relevant information to users. The popularity of pervasively available social activities for social media users has encouraged a large body of literature on exploiting social networks for recommendation. The vast majority of these systems focus on unsigned social networks (or social networks with only positive links), while little work exists for signed social networks (or social networks with positive and negative links). The availability of negative links in signed social networks presents both challenges and opportunities in the recommendation process. We provide a principled and mathematical approach to exploit signed social networks for recommendation, and propose a model, RecSSN, to leverage positive and negative links in signed social networks. Empirical results on real-world datasets demonstrate the effectiveness of the proposed framework. We also perform further experiments to explicitly understand the effect of signed networks in RecSSN.

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

[2]  Paolo Massa,et al.  A Survey of Trust Use and Modeling in Real Online Systems , 2007 .

[3]  Huan Liu,et al.  Is distrust the negation of trust?: the value of distrust in social media , 2014, HT.

[4]  Charu C. Aggarwal,et al.  A Survey of Signed Network Mining in Social Media , 2015, ACM Comput. Surv..

[5]  Yehuda Koren,et al.  Collaborative filtering with temporal dynamics , 2009, KDD.

[6]  Noah E. Friedkin,et al.  Network Studies of Social Influence , 1993 .

[7]  Jennifer Neville,et al.  Modeling relationship strength in online social networks , 2010, WWW '10.

[8]  Mao Ye,et al.  Exploring Social Influence for Recommendation - A Probabilistic Generative Model Approach , 2011, ArXiv.

[9]  LiuJiming,et al.  Community Mining from Signed Social Networks , 2007 .

[10]  Charu C. Aggarwal,et al.  Negative Link Prediction in Social Media , 2014, WSDM.

[11]  Sahin Albayrak,et al.  Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization , 2010, SDM.

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

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

[14]  Michael R. Lyu,et al.  Learning to recommend with trust and distrust relationships , 2009, RecSys '09.

[15]  Mao Ye,et al.  Exploring social influence for recommendation: a generative model approach , 2012, SIGIR '12.

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

[17]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[18]  Norman L. Chervany,et al.  Trust and Distrust Definitions: One Bite at a Time , 2000, Trust in Cyber-societies.

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

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

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

[22]  Ronggong Song,et al.  Trust in E-services: Technologies, Practices and Challenges , 2007 .

[23]  Jérôme Kunegis,et al.  What is the added value of negative links in online social networks? , 2013, WWW.

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

[25]  Chris Cornelis,et al.  Trust- and Distrust-Based Recommendations for Controversial Reviews , 2011, IEEE Intelligent Systems.

[26]  Vincent A. Traag,et al.  Exponential Ranking: Taking into Account Negative Links , 2010, SocInfo.

[27]  Huan Liu,et al.  Trust in social computing , 2014, WWW.

[28]  Jie Tang,et al.  Recommendation in social media: recent advances and new frontiers , 2014, KDD.

[29]  Jiming Liu,et al.  Community Mining from Signed Social Networks , 2007, IEEE Transactions on Knowledge and Data Engineering.

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

[31]  Nagarajan Natarajan,et al.  Exploiting longer cycles for link prediction in signed networks , 2011, CIKM '11.

[32]  J. Cho.  The mechanism of trust and distrust formation and their relational outcomes , 2006 .

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

[34]  Roelof van Zwol,et al.  Flickr tag recommendation based on collective knowledge , 2008, WWW.

[35]  Ee-Peng Lim,et al.  Trust relationship prediction using online product review data , 2009, CIKM-CNIKM.

[36]  Michael Szell,et al.  Multirelational organization of large-scale social networks in an online world , 2010, Proceedings of the National Academy of Sciences.

[37]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

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

[39]  Inderjit S. Dhillon,et al.  Low rank modeling of signed networks , 2012, KDD.

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

[41]  Jure Leskovec,et al.  Predicting positive and negative links in online social networks , 2010, WWW '10.

[42]  B. Lukas,et al.  The equity effect of product endorsement by celebrities: A conceptual framework from a co-branding perspective , 2007 .

[43]  Ramanathan V. Guha,et al.  Propagation of trust and distrust , 2004, WWW '04.