Mixtures of probabilistic principal component analyzers
Neural Computation
Modern Information Retrieval
The Journal of Machine Learning Research
Understanding how bloggers feel: recognizing affect in blog posts
CHI '06 Extended Abstracts on Human Factors in Computing Systems
A music search engine built upon audio-based and web-based similarity measures
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Comparative experiments on sentiment classification for online product reviews
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Automatic mood detection and tracking of music audio signals
IEEE Transactions on Audio, Speech, and Language Processing
Toward Multi-modal Music Emotion Classification
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Matching information content with music
Proceedings of the third ACM conference on Recommender systems
QUC-tree: integrating query context information for efficient music retrieval
IEEE Transactions on Multimedia - Special issue on integration of context and content
New approaches to mood-based hybrid collaborative filtering
Proceedings of the Workshop on Context-Aware Movie Recommendation
Location-adapted music recommendation using tags
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
A haptic emotional model for audio system interface
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: towards mobile and intelligent interaction environments - Volume Part III
Recommending music for places of interest in a mobile travel guide
Proceedings of the fifth ACM conference on Recommender systems
Machine Recognition of Music Emotion: A Review
ACM Transactions on Intelligent Systems and Technology (TIST)
What's the best music you have?: designing music recommendation for group enjoyment in groupfun
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Using emotional context from article for contextual music recommendation
Proceedings of the 21st ACM international conference on Multimedia
Personalized next-song recommendation in online karaokes
Proceedings of the 7th ACM conference on Recommender systems
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In this paper, we present a novel contextual music recommendation approach, MusicSense, to automatically suggest music when users read Web documents such as Weblogs. MusicSense matches music to a document's content, in terms of the emotions expressed by both the document and the music songs. To achieve this, we propose a generative model - Emotional Allocation Modeling - in which a collection of word terms is considered as generated with a mixture of emotions. This model also integrates knowledge discovering from a Web-scale corpus and guidance from psychological studies of emotion. Music songs are also described using textual information extracted from their meta-data and relevant Web pages. Thus, both music songs and Web documents can be characterized as distributions over the emotion mixtures through the emotional allocation modeling. For a given document, the songs with the most matched emotion distributions are finally selected as the recommendations. Preliminary experiments on Weblogs show promising results on both emotion allocation and music recommendation.