Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Content-based retrieval for music collections
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A music recommendation system based on music data grouping and user interests
Proceedings of the tenth international conference on Information and knowledge management
A Personalized Music Filtering System Based on Melody Style Classification
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Customising WAP-based information services on mobile networks
Personal and Ubiquitous Computing
Emotion-based music recommendation by association discovery from film music
Proceedings of the 13th annual ACM international conference on Multimedia
Web image annotation by fusing visual features and textual information
Proceedings of the 2007 ACM symposium on Applied computing
Building a personalized music emotion prediction system
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Automatic mood detection and tracking of music audio signals
IEEE Transactions on Audio, Speech, and Language Processing
Discovering nontrivial repeating patterns in music data
IEEE Transactions on Multimedia
The role of user mood in movie recommendations
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Information Processing and Management: an International Journal
An EEG-Based brain informatics application for enhancing music experience
BI'12 Proceedings of the 2012 international conference on Brain Informatics
Music recommendation using text analysis on song requests to radio stations
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
With the development of digital music technologies, it is an interesting and useful issue to recommend the 'favored music' from large amounts of digital music. Some Web-based music stores can recommend popular music which has been rated by many people. However, three problems that need to be resolved in the current methods are: (a) how to recommend the 'favored music' which has not been rated by anyone, (b) how to avoid repeatedly recommending the 'disfavored music' for users, and (c) how to recommend more interesting music for users besides the ones users have been used to listen. To achieve these goals, we proposed a novel method called personalized hybrid music recommendation, which combines the content-based, collaboration-based and emotion-based methods by computing the weights of the methods according to users' interests. Furthermore, to evaluate the recommendation accuracy, we constructed a system that can recommend the music to users after mining users' logs on music listening records. By the feedback of the user's options, the proposed methods accommodate the variations of the users' musical interests and then promptly recommend the favored and more interesting music via consecutive recommendations. Experimental results show that the recommendation accuracy achieved by our method is as good as 90%. Hence, it is helpful for recommending the 'favored music' to users, provided that each music object is annotated with the related music emotions. The framework in this paper could serve as a useful basis for studies on music recommendation.