A probabilistic music recommender considering user opinions and audio features

A recommender system has an obvious appeal in an environment where the amount of on-line information vastly outstrips any individual's capability to survey. Music recommendation is considered a popular application area. In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focus on capturing precise similarities among users or items based on user historical ratings. Despite the valuable information from audio features of music itself, however, few studies have investigated how to utilize information extracted directly from music for personalized recommendation in CMRS. In this paper, we describe a CMRS based on our proposed item-based probabilistic model, where items are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. In addition, this model has been extended for improved recommendation performance by utilizing audio features that help alleviate three well-known problems associated with data sparseness in collaborative recommender systems: user bias, non-association, and cold start problems in capturing accurate similarities among items. Experimental results based on two real-world data sets lead us to believe that content information is crucial in achieving better personalized recommendation beyond user ratings. We further show how primitive audio features can be combined into aggregate features for the proposed CRMS and analyze their influences on recommendation performance. Although this model was developed originally for music collaborative recommendation based on audio features, our experiment with the movie data set demonstrates that it can be applied to other domains.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  Larry Stead,et al.  Group Asynchronous Browsing on the World Wide Web , 1995, World Wide Web J..

[3]  Pavol Návrat,et al.  Combining Content-Based and Collaborative Filtering , 2000, ADBIS-DASFAA Symposium.

[4]  Donghai Guan,et al.  A music recommender based on audio features , 2004, SIGIR '04.

[5]  Thomas Hofmann,et al.  Collaborative filtering via gaussian probabilistic latent semantic analysis , 2003, SIGIR.

[6]  Arbee L. P. Chen,et al.  A music recommendation system based on music data grouping and user interests , 2001, CIKM '01.

[7]  Ken Goldberg,et al.  Jester 2.0: Evaluation of an New Linear Time Collaborative Filtering Algorithm (poster abstract). , 1999, Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.

[8]  Mark Claypool,et al.  Combining Content-Based and Collaborative Filters in an Online Newspaper , 1999, SIGIR 1999.

[9]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[10]  Jin-Soo Kim,et al.  A New Collaborative Recommender System Addressing Three Problems , 2004, PRICAI.

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

[12]  Naohiro Ishii,et al.  Content-based Collaborative Information Filtering: Actively Learning to Classify and Recommend Documents , 1998, CIA.

[13]  Brian Christopher Smith,et al.  Query by humming: musical information retrieval in an audio database , 1995, MULTIMEDIA '95.

[14]  Richard W. Vuduc,et al.  SWAMI (poster session): a framework for collaborative filtering algorithm development and evaluation , 2000, SIGIR '00.

[15]  Byeong Man Kim,et al.  Constructing User Profiles for Collaborative Recommender System , 2004, APWeb.

[16]  Paul S. Bradley,et al.  Refining Initial Points for K-Means Clustering , 1998, ICML.

[17]  Mark W. Newman,et al.  SWAMI: a framework for collaborative filtering algorithm development and evaluation. , 2000, SIGIR 2000.

[18]  George Tzanetakis,et al.  Musical genre classification of audio signals , 2002, IEEE Trans. Speech Audio Process..

[19]  Jonathan L. Herlocker,et al.  Clustering items for collaborative filtering , 1999 .

[20]  Olfa Nasraoui,et al.  Accurate web recommendations based on profile-specific url-predictor neural networks , 2004, WWW Alt. '04.

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

[22]  Kenneth Y. Goldberg,et al.  Jester 2.0 (poster abstract): evaluation of an new linear time collaborative filtering algorithm , 1999, SIGIR '99.

[23]  Sung-Hyon Myaeng,et al.  Clustering for probabilistic model estimation for CF , 2005, WWW '05.

[24]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[25]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

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

[27]  James C. French,et al.  Flycasting: using collaborative filtering to generate a playlist for online radio , 2001, Proceedings First International Conference on WEB Delivering of Music. WEDELMUSIC 2001.

[28]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[29]  Donna Harman,et al.  Information Processing and Management , 2022 .

[30]  Byeong Man Kim,et al.  Clustering approach for hybrid recommender system , 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003).

[31]  Ahmad M. Ahmad Wasfi.  Collecting user access patterns for building user profiles and collaborative filtering , 1998, IUI '99.