Intelligent profiling by example
Proceedings of the 6th international conference on Intelligent user interfaces
Exploration Strategies for Model-based Learning in Multi-agent Systems: Exploration Strategies
Autonomous Agents and Multi-Agent Systems
Making Rational Decisions Using Adaptive Utility Elicitation
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Coordination in multiagent reinforcement learning: a Bayesian approach
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Distributed collaborative filtering with domain specialization
Proceedings of the 2007 ACM conference on Recommender systems
Feature weighting in content based recommendation system using social network analysis
Proceedings of the 17th international conference on World Wide Web
A Context-Aware Movie Preference Model Using a Bayesian Network for Recommendation and Promotion
UM '07 Proceedings of the 11th international conference on User Modeling
Large-Scale Parallel Collaborative Filtering for the Netflix Prize
AAIM '08 Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Clustering Multivariate Normal Distributions
Emerging Trends in Visual Computing
Personalized movie recommendation
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Modeling item selection and relevance for accurate recommendations: a bayesian approach
Proceedings of the fifth ACM conference on Recommender systems
Model based Bayesian exploration
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
A latent model for collaborative filtering
International Journal of Approximate Reasoning
A sequential recommendation approach for interactive personalized story generation
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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In this paper, we propose a novel Bayesian approach for personalized recommendations. In our approach, we model both user preferences and items under recommendation as multivariate Gaussian distributions; and make use of Normal-Inverse Wishart priors to model the recommendation agent beliefs about user types. We employ a lightweight agent-user interaction process, during which the user is presented with and asked to rate a small number of items. We then interpret these ratings in an innovative way, using them to guide a Bayesian updating process that helps us both capture a user's current mood, and maintain her overall user type. We produced several variants of our approach, and applied them in the movie recommendations domain, evaluating them on data from the MovieLens dataset. Our algorithms are shown to be competitive against a state-of-the-art method, which nevertheless requires a minimum set of ratings from various users to provide recommendations---unlike our entirely personalized approach.