An Homophily-based Approach for Fast Post Recommendation on Twitter

With the unprecedented growth of user-generated content produced on microblogging platforms, finding interesting content for a given user has become a major issue. However due to the intrinsic properties of microblogging systems, such as the vol-umetry, the short lifetime of posts and the sparsity of interactions between users and content, recommender systems cannot rely on traditional methods, such as collaborative filtering matrix factorization. After a thorough study of a large Twitter dataset, we present a propagation model which relies on homophily to propose post recommendations. Our approach relies on the construction of a similarity graph based on retweet behaviors on top of the Twitter graph. Finally we conduct experiments on our real dataset to demonstrate the quality and scalability of our method.

[1]  Hyung Jun Ahn,et al.  A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem , 2008, Inf. Sci..

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

[3]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[4]  Jimmy J. Lin,et al.  WTF: the who to follow service at Twitter , 2013, WWW.

[5]  Mu Zhu,et al.  Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation , 2011, RecSys '11.

[6]  Sebastian Schnettler,et al.  A structured overview of 50 years of small-world research , 2009, Soc. Networks.

[7]  A. Arvidsson,et al.  Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data , 2014 .

[8]  Wendy Liu,et al.  Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors , 2012, ICWSM.

[9]  Benjamin Schrauwen,et al.  Deep content-based music recommendation , 2013, NIPS.

[10]  Kristina Lerman,et al.  Social Contagion: An Empirical Study of Information Spread on Digg and Twitter Follower Graphs , 2012, ArXiv.

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

[12]  Kristina Lerman,et al.  Using Lists to Measure Homophily on Twitter , 2012 .

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

[14]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[15]  Wesley De Neve,et al.  Using topic models for Twitter hashtag recommendation , 2013, WWW.

[16]  Alan Said,et al.  Comparative recommender system evaluation: benchmarking recommendation frameworks , 2014, RecSys '14.

[17]  John Riedl,et al.  An Algorithmic Framework for Performing Collaborative Filtering , 1999, SIGIR Forum.

[18]  Camélia Constantin,et al.  Finding Users of Interest in Micro-blogging Systems , 2016, EDBT.

[19]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[20]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[21]  Jimmy J. Lin,et al.  GraphJet: Real-Time Content Recommendations at Twitter , 2016, Proc. VLDB Endow..

[22]  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.

[23]  W. Bruce Croft,et al.  User oriented tweet ranking: a filtering approach to microblogs , 2011, CIKM '11.

[24]  Yannis Stavrakas,et al.  Tweet and followee personalized recommendations based on knowledge graphs , 2018, J. Ambient Intell. Humaniz. Comput..

[25]  Yang Guo,et al.  Bayesian-Inference-Based Recommendation in Online Social Networks , 2011, IEEE Transactions on Parallel and Distributed Systems.

[26]  Gaël Varoquaux,et al.  Dictionary Learning for Massive Matrix Factorization , 2016, ICML.

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

[28]  Paolo Avesani,et al.  Trust-aware recommender systems , 2007, RecSys '07.

[29]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[30]  Dunja Mladenic,et al.  Real-Time News Recommender System , 2010, ECML/PKDD.

[31]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

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

[33]  Krishna P. Gummadi,et al.  Inferring user interests in the Twitter social network , 2014, RecSys '14.

[34]  Bradley N. Miller,et al.  MovieLens unplugged: experiences with an occasionally connected recommender system , 2003, IUI '03.

[35]  Paul Covington,et al.  Deep Neural Networks for YouTube Recommendations , 2016, RecSys.

[36]  Hsinchun Chen,et al.  Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering , 2004, TOIS.