Document filtering with inference networks
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GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A tutorial on learning with Bayesian networks
Learning in graphical models
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Latent Class Models for Collaborative Filtering
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Language Modeling for Information Retrieval
Language Modeling for Information Retrieval
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A nonparametric hierarchical bayesian framework for information filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Robustness of adaptive filtering methods in a cross-benchmark evaluation
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning Gaussian processes from multiple tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Constructing informative prior distributions from domain knowledge in text classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Bayesian adaptive user profiling with explicit & implicit feedback
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Text classification by labeling words
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Searching and navigating petabyte-scale file systems based on facets
PDSW '07 Proceedings of the 2nd international workshop on Petascale data storage: held in conjunction with Supercomputing '07
Talking the talk vs. walking the walk: salience of information needs in querying vs. browsing
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Boosting collaborative filtering based on statistical prediction errors
Proceedings of the 2008 ACM conference on Recommender systems
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Context Dependent Movie Recommendations Using a Hierarchical Bayesian Model
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Fast nonparametric matrix factorization for large-scale collaborative filtering
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Review: Personalizing recommendations for tourists
Telematics and Informatics
An Information-Theoretic Approach for Multi-task Learning
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Personalization of tagging systems
Information Processing and Management: an International Journal
Recommending new movies: even a few ratings are more valuable than metadata
Proceedings of the third ACM conference on Recommender systems
Learning to recommend with trust and distrust relationships
Proceedings of the third ACM conference on Recommender systems
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A social recommendation framework based on multi-scale continuous conditional random fields
Proceedings of the 18th ACM conference on Information and knowledge management
Discriminative factored prior models for personalized content-based recommendation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Music recommendation by unified hypergraph: combining social media information and music content
Proceedings of the international conference on Multimedia
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
CMAP: effective fusion of quality and relevance for multi-criteria recommendation
Proceedings of the fourth ACM international conference on Web search and data mining
Learning to recommend with explicit and implicit social relations
ACM Transactions on Intelligent Systems and Technology (TIST)
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ACM Transactions on Information Systems (TOIS)
Probabilistic factor models for web site recommendation
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ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Generalizing matrix factorization through flexible regression priors
Proceedings of the fifth ACM conference on Recommender systems
Personalizing group recommendation to social network users
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part I
Bayesian latent variable models for collaborative item rating prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Expert Systems with Applications: An International Journal
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Improving search via personalized query expansion using social media
Information Retrieval
Accelerated singular value thresholding for matrix completion
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-faceted ranking of news articles using post-read actions
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TPDL'12 Proceedings of the Second international conference on Theory and Practice of Digital Libraries
An Investigation of User Behaviour Consistency for Context-Aware Information Retrieval Systems
International Journal of Advanced Pervasive and Ubiquitous Computing
A heuristic method for collaborative recommendation using hierarchical user profiles
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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You are what you consume: a bayesian method for personalized recommendations
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Preference-based mining of top-K influential nodes in social networks
Future Generation Computer Systems
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World Wide Web
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A content-based personalized recommendation system learns user specific profiles from user feedback so that it can deliver information tailored to each individual user's interest. A system serving millions of users can learn a better user profile for a new user, or a user with little feedback, by borrowing information from other users through the use of a Bayesian hierarchical model. Learning the model parameters to optimize the joint data likelihood from millions of users is very computationally expensive. The commonly used EM algorithm converges very slowly due to the sparseness of the data in IR applications. This paper proposes a new fast learning technique to learn a large number of individual user profiles. The efficacy and efficiency of the proposed algorithm are justified by theory and demonstrated on actual user data from Netflix and MovieLens.