A Space Alignment Method for Cold-Start TV Show Recommendations

In recent years, recommendation algorithms have become one of the most active research areas driven by the enormous industrial demands. Most of the existing recommender systems focus on topics such as movie, music, e-commerce etc., which essentially differ from the TV show recommendations due to the cold-start and temporal dynamics. Both effectiveness (effectively handling the cold-start TV shows) and efficiency (efficiently updating the model to reflect the temporal data changes) concerns have to be addressed to design real-world TV show recommendation algorithms. In this paper, we introduce a novel hybrid recommendation algorithm incorporating both collaborative user-item relationship as well as item content features. The cold-start TV shows can be correctly recommended to desired users via a so called space alignment technique. On the other hand, an online updating scheme is developed to utilize new user watching behaviors. We present experimental results on a real TV watch behavior data set to demonstrate the significant performance improvement over other state-of-the-art algorithms.

[1]  George Karypis,et al.  SLIM: Sparse Linear Methods for Top-N Recommender Systems , 2011, 2011 IEEE 11th International Conference on Data Mining.

[2]  B. C. Brookes,et al.  Information Sciences , 2020, Cognitive Skills You Need for the 21st Century.

[3]  Susumu Horiguchi,et al.  Learning to classify short and sparse text & web with hidden topics from large-scale data collections , 2008, WWW.

[4]  Yoram Singer,et al.  Online and batch learning of pseudo-metrics , 2004, ICML.

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

[6]  Yurii Nesterov,et al.  Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.

[7]  Yehuda Koren,et al.  Lessons from the Netflix prize challenge , 2007, SKDD.

[8]  Nicholas I. M. Gould,et al.  SIAM Journal on Optimization , 2012 .

[9]  Suhrid Balakrishnan,et al.  Collaborative ranking , 2012, WSDM '12.

[10]  Royi Ronen,et al.  Selecting content-based features for collaborative filtering recommenders , 2013, RecSys.

[11]  Alok N. Choudhary,et al.  Elver: Recommending Facebook pages in cold start situation without content features , 2013, 2013 IEEE International Conference on Big Data.

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

[13]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[14]  Charu C. Aggarwal,et al.  Factorized Similarity Learning in Networks , 2014, 2014 IEEE International Conference on Data Mining.

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

[16]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[17]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

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

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

[20]  Emmanuel J. Candès,et al.  A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..

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

[22]  Inderjit S. Dhillon,et al.  Online Metric Learning and Fast Similarity Search , 2008, NIPS.

[23]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[24]  Yehuda Koren,et al.  Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy , 2011, RecSys '11.

[25]  S. Crawford,et al.  Volume 1 , 2012, Journal of Diabetes Investigation.

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

[27]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[28]  Francesco Ricci,et al.  Rating Elicitation Strategies for Collaborative Filtering , 2011, EC-Web.

[29]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

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

[31]  Chao Liu,et al.  Wisdom of the better few: cold start recommendation via representative based rating elicitation , 2011, RecSys '11.

[32]  Patrick J. Roa.  Volume 8 , 2001 .

[33]  Lars Schmidt-Thieme,et al.  Learning Attribute-to-Feature Mappings for Cold-Start Recommendations , 2010, 2010 IEEE International Conference on Data Mining.

[34]  Anmol Bhasin,et al.  Generating supplemental content information using virtual profiles , 2013, RecSys.

[35]  Inderjit S. Dhillon,et al.  Information-theoretic metric learning , 2006, ICML '07.

[36]  Xi Zhang,et al.  Semi-supervised discriminative preference elicitation for cold-start recommendation , 2013, CIKM.

[37]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.