GhostLink: Latent Network Inference for Influence-aware Recommendation

Social influence plays a vital role in shaping a user's behavior in online communities dealing with items of fine taste like movies, food, and beer. For online recommendation, this implies that users' preferences and ratings are influenced due to other individuals. Given only time-stamped reviews of users, can we find out who-influences-whom, and characteristics of the underlying influence network? Can we use this network to improve recommendation? While prior works in social-aware recommendation have leveraged social interaction by considering the observed social network of users, many communities like Amazon, Beeradvocate, and Ratebeer do not have explicit user-user links. Therefore, we propose GhostLink, an unsupervised probabilistic graphical model, to automatically learn the latent influence network underlying a review community - given only the temporal traces (timestamps) of users' posts and their content. Based on extensive experiments with four real-world datasets with 13 million reviews, we show that GhostLink improves item recommendation by around 23% over state-of-the-art methods that do not consider this influence. As additional use-cases, we show that GhostLink can be used to differentiate between users' latent preferences and influenced ones, as well as to detect influential users based on the learned influence graph.

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

[2]  Chunyan Miao,et al.  A social influence based trust model for recommender systems , 2017, Intell. Data Anal..

[3]  Jure Leskovec,et al.  On the Convexity of Latent Social Network Inference , 2010, NIPS.

[4]  Xi Chen,et al.  Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization , 2010, SDM.

[5]  Christos Faloutsos,et al.  Robust multivariate autoregression for anomaly detection in dynamic product ratings , 2014, WWW.

[6]  Yue Lu,et al.  Latent aspect rating analysis on review text data: a rating regression approach , 2010, KDD.

[7]  Bernhard Schölkopf,et al.  Uncovering the Temporal Dynamics of Diffusion Networks , 2011, ICML.

[8]  Ken Goldberg,et al.  Social Influence Bias in Recommender Systems : A Methodology for Learning , Analyzing , and Mitigating Bias in Ratings , 2014 .

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

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

[11]  Junpeng Guo,et al.  A Social Influence Approach for Group User Modeling in Group Recommendation Systems , 2016, IEEE Intelligent Systems.

[12]  Sanjay Krishnan,et al.  A methodology for learning, analyzing, and mitigating social influence bias in recommender systems , 2014, RecSys '14.

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

[14]  Le Song,et al.  Uncover Topic-Sensitive Information Diffusion Networks , 2013, AISTATS.

[15]  Yehuda Koren,et al.  Advances in Collaborative Filtering , 2011, Recommender Systems Handbook.

[16]  Huan Liu,et al.  mTrust: discerning multi-faceted trust in a connected world , 2012, WSDM '12.

[17]  Jure Leskovec,et al.  From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews , 2013, WWW.

[18]  Christos Faloutsos,et al.  Detecting anomalies in dynamic rating data: a robust probabilistic model for rating evolution , 2014, KDD.

[19]  이주연,et al.  Latent Dirichlet Allocation (LDA) 모델 기반의 인공지능(A.I.) 기술 관련 연구 활동 및 동향 분석 , 2018 .

[20]  Jure Leskovec,et al.  Inferring networks of diffusion and influence , 2010, KDD.

[21]  Jimeng Sun,et al.  Temporal recommendation on graphs via long- and short-term preference fusion , 2010, KDD.

[22]  Philippe Preux,et al.  Exploiting Social Information in Pairwise Preference Recommender System , 2016, J. Inf. Data Manag..

[23]  Yue Lu,et al.  Latent aspect rating analysis without aspect keyword supervision , 2011, KDD.

[24]  Laks V. S. Lakshmanan,et al.  Learning influence probabilities in social networks , 2010, WSDM '10.

[25]  Zhoujun Li,et al.  Diabetes-Associated Factors as Predictors of Nursing Home Admission and Costs in the Elderly Across Europe. , 2017, Journal of the American Medical Directors Association.

[26]  Kun Yang,et al.  Social Recommendation with Interpersonal Influence , 2010, ECAI.

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

[28]  Jure Leskovec,et al.  Information diffusion and external influence in networks , 2012, KDD.

[29]  Subhabrata Mukherjee,et al.  Joint Author Sentiment Topic Model , 2014, SDM.

[30]  Steffen Bickel,et al.  Unsupervised prediction of citation influences , 2007, ICML '07.

[31]  Chen Lin,et al.  Personalized news recommendation via implicit social experts , 2014, Inf. Sci..

[32]  Hao Ma,et al.  An experimental study on implicit social recommendation , 2013, SIGIR.

[33]  James R. Foulds,et al.  HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades , 2015, ICML.

[34]  Ruslan Salakhutdinov,et al.  Evaluation methods for topic models , 2009, ICML '09.

[35]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[36]  Gerhard Weikum,et al.  Continuous Experience-aware Language Model , 2016, KDD.

[37]  Chengqi Zhang,et al.  Inferring Latent Network from Cascade Data for Dynamic Social Recommendation , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

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

[39]  Jiawei Han,et al.  Learning influence from heterogeneous social networks , 2012, Data Mining and Knowledge Discovery.

[40]  Thomas L. Griffiths,et al.  The Author-Topic Model for Authors and Documents , 2004, UAI.

[41]  Daniel Thalmann,et al.  From ratings to trust: an empirical study of implicit trust in recommender systems , 2014, SAC.

[42]  Chirag Shah,et al.  Collaborative User Network Embedding for Social Recommender Systems , 2017, SDM.