Computational Media Aesthetics: Finding Meaning Beautiful
IEEE MultiMedia
Personalized active learning for collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A Regression Approach to Music Emotion Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Machine Recognition of Music Emotion: A Review
ACM Transactions on Intelligent Systems and Technology (TIST)
Personalized music emotion classification via active learning
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
| Hi-index | 0.00 |
In recent years, there has been a dramatic proliferation of research on information retrieval based on highly subjective concepts such as emotion, preference and aesthetic. Such retrieval methods are fascinating but challenging since it is difficult to built a general retrieval model that performs equally well to everyone. In this paper, we propose two novel methods, bag-of-users model and residual modeling, to accommodate the individual differences for emotion-based music retrieval. The proposed methods are intuitive and generally applicable to other information retrieval tasks that involve subjective perception. Evaluation result shows the effectiveness of the proposed methods.