The Journal of Machine Learning Research
Music Information Retrieval by Detecting Mood via Computational Media Aesthetics
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
The Journal of Machine Learning Research
Inferring similarity between music objects with application to playlist generation
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
A new Mallows distance based metric for comparing clusterings
ICML '05 Proceedings of the 22nd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Applying latent dirichlet allocation to group discovery in large graphs
Proceedings of the 2009 ACM symposium on Applied Computing
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Collaborative prediction and ranking with non-random missing data
Proceedings of the third ACM conference on Recommender systems
Joint group and topic discovery from relations and text
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Ranking in context-aware recommender systems
Proceedings of the 20th international conference companion on World wide web
Build your own music recommender by modeling internet radio streams
Proceedings of the 21st international conference on World Wide Web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Playlist prediction via metric embedding
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Query-driven context aware recommendation
Proceedings of the 7th ACM conference on Recommender systems
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User experience in social media involves rich interactions with the media content and other participants in the community. In order to support such communities, it is important to understand the factors that drive the users' engagement. In this paper we show how to define statistical models of different complexity to describe patterns of song listening in an online music community. First, we adapt the LDA model to capture music taste from listening activities across users and identify both the groups of songs associated with the specific taste and the groups of listeners who share the same taste. Second, we define a graphical model that takes into account listening sessions and captures the listening moods of users in the community. Our session model leads to groups of songs and groups of listeners with similar behavior across listening sessions and enables faster inference when compared to the LDA model. Our experiments with the data from an online media site demonstrate that the session model is better in terms of the perplexity compared to two other models: the LDA-based taste model that does not incorporate cross-session information and a baseline model that does not use latent groupings of songs.