Discovering What You're Known For: A Contextual Poisson Factorization Approach

Discovering what people are known for is valuable to many important applications such as recommender systems. Unlike an individual's personal interests, what a user is known for is reflected by the views of others, and is often not easily discerned for a long-tail of the vast majority of users. In this paper, we tackle the problem of discovering what users are known for through a probabilistic model called Bayesian Contextual Poisson Factorization. Moving beyond just modeling user's content, it naturally models and integrates additional contextual factors, concretely, user's geo-spatial footprints and social influence, to overcome noisy online activities and social relations. Through GPS-tagged social media datasets, we find that the proposed method can improve known-for prediction performance by 17.5% in precision and 20.9% in recall on average, and that it can capture the implicit relationships between a user's known-for profile and her content, geo-spatial and social influence.

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

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

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

[4]  J. Davis.  Univariate Discrete Distributions , 2006 .

[5]  Yong Liu,et al.  Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction , 2014, SIGIR.

[6]  David M. Blei,et al.  Scalable Recommendation with Poisson Factorization , 2013, ArXiv.

[7]  A. W. Kemp,et al.  Univariate Discrete Distributions: Johnson/Univariate Discrete Distributions , 2005 .

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

[9]  A. W. Kemp,et al.  Univariate Discrete Distributions , 1993 .

[10]  Yehuda Koren,et al.  Care to comment?: recommendations for commenting on news stories , 2012, WWW.

[11]  Jason Weston,et al.  WSABIE: Scaling Up to Large Vocabulary Image Annotation , 2011, IJCAI.

[12]  Laks V. S. Lakshmanan,et al.  HeteroMF: recommendation in heterogeneous information networks using context dependent factor models , 2013, WWW.

[13]  Hui Xiong,et al.  Learning geographical preferences for point-of-interest recommendation , 2013, KDD.

[14]  Xing Xie,et al.  Collaborative location and activity recommendations with GPS history data , 2010, WWW '10.

[15]  Zi Huang,et al.  A temporal context-aware model for user behavior modeling in social media systems , 2014, SIGMOD Conference.

[16]  James Caverlee,et al.  Exploiting Geo-Spatial Preference for Personalized Expert Recommendation , 2015, RecSys.

[17]  Chong Wang,et al.  Stochastic variational inference , 2012, J. Mach. Learn. Res..

[18]  Krishna P. Gummadi,et al.  You are who you know: inferring user profiles in online social networks , 2010, WSDM '10.

[19]  Eduard H. Hovy,et al.  Weakly Supervised User Profile Extraction from Twitter , 2014, ACL.

[20]  Ed H. Chi,et al.  Speak little and well: recommending conversations in online social streams , 2011, CHI.

[21]  Krishna P. Gummadi,et al.  Deep Twitter diving: exploring topical groups in microblogs at scale , 2014, CSCW.

[22]  Xavier Amatriain,et al.  The wisdom of the few: a collaborative filtering approach based on expert opinions from the web , 2009, SIGIR.

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

[24]  Feng Qiu,et al.  Automatic identification of user interest for personalized search , 2006, WWW '06.

[25]  Krishna P. Gummadi,et al.  Cognos: crowdsourcing search for topic experts in microblogs , 2012, SIGIR '12.

[26]  Geoffrey J. Gordon,et al.  Relational learning via collective matrix factorization , 2008, KDD.

[27]  Alexander J. Smola,et al.  Scalable distributed inference of dynamic user interests for behavioral targeting , 2011, KDD.

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

[29]  Xi Zhang,et al.  TopRec: domain-specific recommendation through community topic mining in social network , 2013, WWW '13.

[30]  Wei Zhang,et al.  Combining latent factor model with location features for event-based group recommendation , 2013, KDD.

[31]  David M. Blei,et al.  Content-based recommendations with Poisson factorization , 2014, NIPS.

[32]  Chun Chen,et al.  Whom to mention: expand the diffusion of tweets by @ recommendation on micro-blogging systems , 2013, WWW '13.

[33]  Ji-Rong Wen,et al.  WWW 2007 / Track: Search Session: Personalization A Largescale Evaluation and Analysis of Personalized Search Strategies ABSTRACT , 2022 .

[34]  Kevin Chen-Chuan Chang,et al.  User profiling in an ego network: co-profiling attributes and relationships , 2014, WWW.

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

[36]  Zhe Zhao,et al.  Improving User Topic Interest Profiles by Behavior Factorization , 2015, WWW.

[37]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[38]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .