Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Clustering Approach for Hybrid Recommender System
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
A music recommender based on audio features
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Accurate web recommendations based on profile-specific url-predictor neural networks
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A probabilistic music recommender considering user opinions and audio features
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Inferring user's preferences using ontologies
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
MUADDIB: A distributed recommender system supporting device adaptivity
ACM Transactions on Information Systems (TOIS)
Document clustering with universum
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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Based on the type of collaborative objects, a collaborative filtering (CF) system falls into one of two categories: item-based CF and user-based CF. Clustering is the basic idea in both cases, where users or items are classified into user groups where users share similar preference or item groups where items have similar attributes or characteristics. Observing the fact that in user-based CF each user community is characterized by a Gaussian distribution on the ratings for each item and the fact that in item-based CF the ratings of each user in item community satisfy a Gaussian distribution, we propose a method of probabilistic model estimation for CF, where objects (user or items) are classified into groups based on the content information and ratings at the same time and predictions are made considering the Gaussian distribution of ratings. Experiments on a real-world data set illustrate that our approach is favorable.