Watch-It-Next: A Contextual TV Recommendation System

As consumers of television are presented with a plethora of available programming, improving recommender systems in this domain is becoming increasingly important. Television sets, though, are often shared by multiple users whose tastes may greatly vary. Recommendation systems are challenged by this setting, since viewing data is typically collected and modeled per device, aggregating over its users and obscuring their individual tastes. This paper tackles the challenge of TV recommendation, specifically aiming to provide recommendations for the next program to watch following the currently watched program the device. We present an empirical evaluation of several recommendation methods over large-scale, real-life TV viewership data. Our extentions of common state-of-the-art recommendation methods, exploiting the current watching context, demonstrate a significant improvement in recommendation quality.

[1]  Irena Koprinska,et al.  Catch-up TV recommendations: show old favourites and find new ones , 2013, RecSys.

[2]  Lars Schmidt-Thieme,et al.  Fast context-aware recommendations with factorization machines , 2011, SIGIR.

[3]  Balázs Hidasi,et al.  Personalized recommendation of linear content on interactive TV platforms: beating the cold start and noisy implicit user feedback , 2012, UMAP Workshops.

[4]  Alexandros Karatzoglou,et al.  Collaborative temporal order modeling , 2011, RecSys '11.

[5]  Linas Baltrunas,et al.  Towards Time-Dependant Recommendation based on Implicit Feedback , 2009 .

[6]  Vanja Josifovski,et al.  Up next: retrieval methods for large scale related video suggestion , 2014, KDD.

[7]  Francesco Ricci,et al.  Context-based splitting of item ratings in collaborative filtering , 2009, RecSys '09.

[8]  Ryen W. White,et al.  From devices to people: attribution of search activity in multi-user settings , 2014, WWW.

[9]  Bernd Ludwig,et al.  Matrix factorization techniques for context aware recommendation , 2011, RecSys '11.

[10]  Roberto Turrin,et al.  A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment , 2011, Recommender Systems Handbook.

[11]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[12]  Enhong Chen,et al.  Context-aware ranking in web search , 2010, SIGIR '10.

[13]  Enhong Chen,et al.  Towards context-aware search by learning a very large variable length hidden markov model from search logs , 2009, WWW '09.

[14]  Chong Wang,et al.  Latent Collaborative Retrieval , 2012, ICML.

[15]  Gediminas Adomavicius,et al.  Incorporating contextual information in recommender systems using a multidimensional approach , 2005, TOIS.

[16]  Martha Larson,et al.  TFMAP: optimizing MAP for top-n context-aware recommendation , 2012, SIGIR '12.

[17]  Nuria Oliver,et al.  Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering , 2010, RecSys '10.

[18]  Balázs Hidasi,et al.  Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback , 2012, ECML/PKDD.

[19]  Francesco Ricci,et al.  Context-Aware Recommender Systems , 2011, AI Mag..

[20]  Sahin Albayrak,et al.  Inferring Contextual User Profiles - Improving Recommender Performance , 2011 .

[21]  Lora Aroyo,et al.  User model elicitation and enrichment for context-sensitive personalization in a multiplatform tv environment , 2009, EuroITV '09.

[22]  Yu He,et al.  The YouTube video recommendation system , 2010, RecSys '10.

[23]  Yanchun Zhang,et al.  Modelling User Behaviour for Web Recommendation Using LDA Model , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[24]  Stratis Ioannidis,et al.  Guess Who Rated This Movie: Identifying Users Through Subspace Clustering , 2012, UAI.

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

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

[27]  Bamshad Mobasher,et al.  Query-driven context aware recommendation , 2013, RecSys.

[28]  John Zimmerman,et al.  TV Personalization System , 2004, Personalized Digital Television.