Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Variational Extensions to EM and Multinomial PCA
ECML '02 Proceedings of the 13th European Conference on Machine Learning
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Bayesian Analysis of Online Newspaper Log Data
SAINT-W '03 Proceedings of the 2003 Symposium on Applications and the Internet Workshops (SAINT'03 Workshops)
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
The multiple multiplicative factor model for collaborative filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Bayesian approach toward active learning for collaborative filtering
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Maximum entropy for collaborative filtering
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Combining eye movements and collaborative filtering for proactive information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic clustering-projection model for discrete data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Two-Way Grouping by One-Way Topic Models
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
A social recommendation framework based on multi-scale continuous conditional random fields
Proceedings of the 18th ACM conference on Information and knowledge management
Discovering different types of topics: factored topic models
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We tackle the problem of new users or documents in collaborative filtering. Generalization over users by grouping them into user groups is beneficial when a rating is to be predicted for a relatively new document having only few observed ratings. Analogously, generalization over documents improves predictions in the case of new users. We show that if either users and documents or both are new, two-way generalization becomes necessary. We demonstrate the benefits of grouping of users, grouping of documents, and two-way grouping, with artificial data and in two case studies with real data. We have introduced a probabilistic latent grouping model for predicting the relevance of a document to a user. The model assumes a latent group structure for both users and items. We compare the model against a state-of-the-art method, the User Rating Profile model, where only the users have a latent group structure. We compute the posterior of both models by Gibbs sampling. The Two-Way Model predicts relevance more accurately when the target consists of both new documents and new users. The reason is that generalization over documents becomes beneficial for new documents and at the same time generalization over users is needed for new users.