GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
IEEE Transactions on Pattern Analysis and Machine Intelligence
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
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
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
A user-item relevance model for log-based collaborative filtering
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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Recommender Systems (RS) are applications that provide personalized advice to users about products or services they might be interested in. To improve recommendation quality, many hybridization techniques have been proposed. Among all hybrids, the weighted recommenders have the main benefit that all of the system's constituents operate independently and stand in a straightforward way over the recommendation process. However, the hybrids proposed so far consist of a linear combination of the final scores resulting from all recommendation techniques available. Thus, they fail to provide explanations of predictions or further insights into the data. In this work, we propose a theoretical framework to combine information using the two basic probabilistic schemes: the sum and product rule. Extensive experiments have shown that our purely probabilistic schemes provide better quality recommendations compared to other methods that combine numerical scores derived from each prediction method individually.