Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Clustering Approach for Hybrid Recommender System
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Probabilistic Model Estimation for Collaborative Filtering Based on Items Attributes
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Clustering for probabilistic model estimation for CF
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Music retrieval: a tutorial and review
Foundations and Trends in Information Retrieval
A probabilistic music recommender considering user opinions and audio features
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Scalable music recommendation by search
Proceedings of the 15th international conference on Multimedia
A hybrid social-acoustic recommendation system for popular music
Proceedings of the 2007 ACM conference on Recommender systems
COLLABORATIVE WEB AGENT BASED ON FRIEND NETWORK
Applied Artificial Intelligence
Music recommendation based on acoustic features and user access patterns
IEEE Transactions on Audio, Speech, and Language Processing
Social navigation for the spoken web
Proceedings of the fourth ACM conference on Recommender systems
A context-aware music recommendation agent in smart office
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
A probabilistic model for music recommendation considering audio features
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
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Many collaborative music recommender systems (CMRS) have succeeded in capturing the similarity among users or items based on ratings, however they have rarely considered about the available information from the multimedia such as genres, let alone audio features from the media stream. Such information is valuable and can be used to solve several problems in RS. In this paper, we design a CMRS based on audio features of the multimedia stream. In the CMRS, we provide recommendation service by our proposed method where a clustering technique is used to integrate the audio features of music into the collaborative filtering (CF) framework in hopes of achieving better performance. Experiments are carried out to demonstrate that our approach is feasible.