Improving Quality of Service for Radio Station Hosting: An Online Recommender System Based on Information Fusion

We present a new recommender system developed for the Russian interactive radio network FMhost. The system aims to improve the quality of this service; it is designed specifically to deal with small datasets, overcoming the shortage of data on observed user behavior. The underlying model combines a collaborative user-based approach with information from tags of listened tracks in order to match user and radio station profiles. It follows an adaptive online learning strategy based on both user history and implicit feedback. We compare the proposed algorithms with industry standard methods based on Singular Value Decomposition (SVD) in terms of precision, recall, and Normalized Discounted Cumulative Gain (NDCG) measures; experiments show that in our case the fusion-based approach produces the best results.

[1]  Rong Pan,et al.  Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering , 2009, KDD.

[2]  Andreas Butz,et al.  AudioRadar: A Metaphorical Visualization for the Navigation of Large Music Collections , 2006, Smart Graphics.

[3]  Yi-Hsuan Yang,et al.  Music retagging using label propagation and robust principal component analysis , 2012, WWW.

[4]  Daniel Ch. Buser.  Context-Based Recommender Systems in Conventional Grocery--An Economic Analysis , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[5]  Òscar Celma,et al.  Music recommendation and discovery in the long tail , 2008 .

[6]  Peter Knees,et al.  Towards Semantic Music Information Extraction from the Web Using Rule Patterns and Supervised Learning , 2011 .

[7]  Paul Lamere,et al.  Workshop report: WOMRAD 2010 , 2010, RecSys '10.

[8]  Seventh ACM Conference on Recommender Systems, RecSys '13, Hong Kong, China, October 12-16, 2013 , 2013, RecSys.

[9]  Loredana Mocean,et al.  Marketing Recommender Systems: A New Approach in Digital Economy , 2012 .

[10]  Yehuda Koren,et al.  Build your own music recommender by modeling internet radio streams , 2012, WWW.

[11]  Paul Lamere,et al.  Music recommendation and discovery revisited , 2011, RecSys '11.

[12]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[13]  Yehuda Koren,et al.  Factor in the neighbors: Scalable and accurate collaborative filtering , 2010, TKDD.

[14]  Michael R. Lyu,et al.  Semi-nonnegative matrix factorization with global statistical consistency for collaborative filtering , 2009, CIKM.

[15]  Ye Wang,et al.  Large-scale music tag recommendation with explicit multiple attributes , 2010, ACM Multimedia.

[16]  Jonas Poelmans,et al.  A New Cross-Validation Technique to Evaluate Quality of Recommender Systems , 2012, PerMIn.

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

[18]  Byung-Do Kim,et al.  A new recommender system to combine content-based and collaborative filtering systems , 2001 .

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

[20]  Yehuda Koren,et al.  Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[21]  Jianying Hu,et al.  One-Class Matrix Completion with Low-Density Factorizations , 2010, 2010 IEEE International Conference on Data Mining.

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

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

[24]  Carlos Soares,et al.  Monitoring Recommender Systems: A Business Intelligence Approach , 2014, ICCSA.

[25]  İpek Tatlı,et al.  Using Semantic Relations in Context-based Music Recommendations , 2011 .

[26]  Ziming Zeng.  An Intelligent E-commerce Recommender System Based on Web Mining , 2009 .

[27]  Sergey I. Nikolenko,et al.  Online Recommender System for Radio Station Hosting , 2012, BIR.

[28]  Paul Lamere,et al.  WOMRAD: 2nd workshop on music recommendation and discovery , 2011, RecSys '11.

[29]  Emilia Gómez,et al.  Semantic audio content-based music recommendation and visualization based on user preference examples , 2013, Inf. Process. Manag..

[30]  Douglas Eck,et al.  Aggregate features and ADABOOST for music classification , 2006, Machine Learning.

[31]  Bart P. Knijnenburg,et al.  Explaining the user experience of recommender systems , 2012, User Modeling and User-Adapted Interaction.

[32]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[33]  Cheng-Jung Lin,et al.  A recommender system to avoid customer churn: A case study , 2009, Expert Syst. Appl..

[34]  Jakob Abeßer,et al.  Music Search and Recommendation , 2009, Handbook of Multimedia for Digital Entertainment and Arts.

[35]  Mitsunori Ogihara,et al.  NextOne Player: A Music Recommendation System Based on User Behavior , 2011, ISMIR.

[36]  Yuefeng Li,et al.  The state-of-the-art in personalized recommender systems for social networking , 2012, Artificial Intelligence Review.

[37]  Feng Niu,et al.  Million Song Dataset Challenge ! , 2012 .

[38]  ChengXiang Zhai,et al.  Improving one-class collaborative filtering by incorporating rich user information , 2010, CIKM.

[39]  György Fazekas,et al.  Music recommendation for music learning: Hotttabs, a multimedia guitar tutor , 2011 .

[40]  Thierry Bertin-Mahieux,et al.  Automatic Generation of Social Tags for Music Recommendation , 2007, NIPS.

[41]  Ruslan Salakhutdinov,et al.  Bayesian probabilistic matrix factorization using Markov chain Monte Carlo , 2008, ICML '08.

[42]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[43]  Jonas Poelmans,et al.  Can triconcepts become triclusters? , 2013, Int. J. Gen. Syst..

[44]  F. Maxwell Harper,et al.  An Economic Model of User Rating in an Online Recommender System , 2005, User Modeling.

[45]  Proceedings of the 21st World Wide Web Conference, WWW 2012, Lyon, France, April 16-20, 2012 (Companion Volume) , 2012, WWW.

[46]  Angelo Susi,et al.  Collaborative Radio Community , 2002, AH.

[48]  Alexander Tuzhilin,et al.  The effect of context-aware recommendations on customer purchasing behavior and trust , 2011, RecSys '11.

[49]  Sahin Albayrak,et al.  Increasing Diversity Through Furthest Neighbor-Based Recommendation , 2012 .

[50]  Panagiotis Symeonidis,et al.  Ternary Semantic Analysis of Social Tags for Personalized Music Recommendation , 2008, ISMIR.

[51]  S. C. Hui,et al.  Web content recommender system based on consumer behavior modeling , 2011, IEEE Transactions on Consumer Electronics.

[52]  Panagiotis Symeonidis,et al.  MusicBox: Personalized Music Recommendation Based on Cubic Analysis of Social Tags , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[53]  Panagiotis Symeonidis,et al.  Nearest-biclusters collaborative filtering based on constant and coherent values , 2008, Information Retrieval.

[54]  Xavier Serra,et al.  Folksonomy-based Tag Recommendation for Online Audio Clip Sharing , 2012, ISMIR.

[55]  Maurizio Vichi,et al.  Studies in Classification Data Analysis and knowledge Organization , 2011 .

[56]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

[57]  Dmitry Bogdanov,et al.  How Much Metadata Do We Need in Music Recommendation? A Subjective Evaluation Using Preference Sets , 2011, ISMIR.

[58]  Ian Knopke.  The Importance of Service and Genre in Recommendations for Online Radio and Television Programmes , 2011 .

[59]  D. S. Sivia,et al.  Data Analysis , 1996, Encyclopedia of Evolutionary Psychological Science.

[60]  David F. Gleich,et al.  The World of Music: SDP layout of high dimensional data , 2005 .

[61]  Francesco Ricci,et al.  Optimal radio channel recommendations with explicit and implicit feedback , 2012, RecSys.

[62]  Leonid Zhukov,et al.  The World of Music : User Ratings ; Spectral and Spherical Embeddings ; Map Projections , 2006 .

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

[64]  Hui Chen,et al.  The Impact of Comments and Recommendation System on Online Shopper Buying Behaviour , 2012, J. Networks.

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

[66]  Xi Zhang,et al.  A Weighted One Class Collaborative Filtering with Content Topic Features , 2013, MMM.

[67]  Òscar Celma,et al.  Music Recommendation and Discovery - The Long Tail, Long Fail, and Long Play in the Digital Music Space , 2010 .

[68]  Dirk Bergemann,et al.  Optimal pricing with recommender systems , 2006, EC '06.

[69]  Qiang Yang,et al.  One-Class Collaborative Filtering , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[70]  John Riedl,et al.  Recommender systems: from algorithms to user experience , 2012, User Modeling and User-Adapted Interaction.

[71]  Simon Dixon,et al.  Music Discovery with Social Networks , 2011 .

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

[73]  Michael Hahsler,et al.  myVU: A Next Generation Recommender System Based on Observed Consumer Behavior and Interactive Evolutionary Algorithms , 2000 .