Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
A maximum entropy web recommendation system: combining collaborative and content features
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
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
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
An analysis of probabilistic methods for top-N recommendation in collaborative filtering
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Regularized Gibbs Sampling for User Profiling with Soft Constraints
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
You are what you consume: a bayesian method for personalized recommendations
Proceedings of the 7th ACM conference on Recommender systems
Probabilistic topic models for sequence data
Machine Learning
Hi-index | 0.00 |
We propose a bayesian probabilistic model for explicit preference data. The model introduces a generative process, which takes into account both item selection and rating emission to gather into communities those users who experience the same items and tend to adopt the same rating pattern. Each user is modeled as a random mixture of topics, where each topic is characterized by a distribution modeling the popularity of items within the respective user-community and by a distribution over preference values for those items. The proposed model can be associated with a novel item-relevance ranking criterion, which is based both on item popularity and user's preferences. We show that the proposed model, equipped with the new ranking criterion, outperforms state-of-art approaches in terms of accuracy of the recommendation list provided to users on standard benchmark datasets.