GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Query by humming: musical information retrieval in an audio database
Proceedings of the third ACM international conference on Multimedia
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
Collecting user access patterns for building user profiles and collaborative filtering
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
Jester 2.0 (poster abstract): evaluation of an new linear time collaborative filtering algorithm
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
SWAMI (poster session): a framework for collaborative filtering algorithm development and evaluation
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Data mining: concepts and techniques
Data mining: concepts and techniques
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
CIA '98 Proceedings of the Second International Workshop on Cooperative Information Agents II, Learning, Mobility and Electronic Commerce for Information Discovery on the Internet
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Flycasting: Using Collaborative Filtering to Generate a Playlist for Online Radio
WEDELMUSIC '01 Proceedings of the First International Conference on WEB Delivering of Music (WEDELMUSIC'01)
Clustering Approach for Hybrid Recommender System
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
A music recommender based on audio features
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
International Journal of Learning Technology
Using Evolving Agents to Critique Subjective Music Compositions
Computational Intelligence and Security
Web-Based Recommender Systems and User Needs --the Comprehensive View
Proceedings of the 2008 conference on New Trends in Multimedia and Network Information Systems
MusicBox: personalized music recommendation based on cubic analysis of social tags
IEEE Transactions on Audio, Speech, and Language Processing
Evaluating subjective compositions by the cooperation between human and adaptive agents
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Information Processing and Management: an International Journal
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In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focused 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 directly extract and utilize information 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. By utilizing audio features, this model provides a way to alleviate three well-known challenges in collaborative recommender systems: user bias, non-association, and cold start problems in capturing accurate similarities among items. Experiments on a real-world data set illustrate that the audio information of music is quite useful and our system is feasible to integrate it for better personalized recommendation.