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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.
@inproceedings{1009106, author = {Qing Li and Byeong Man Kim and Dong Hai Guan and Duk whan Oh}, title = {A music recommender based on audio features}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {532--533}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009106}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
