Fab: content-based, collaborative recommendation
Communications of the ACM
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
An iterative algorithm for trust and reputation management
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 3
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
On decoding of low-density parity-check codes over the binary erasure channel
IEEE Transactions on Information Theory
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In this paper we report our progress in the first application of iterative probabilistic algorithms in the design and evaluation of recommender systems. The proposed iterative recommender system (referred to as BPRS) is based on the belief propagation, a powerful decoding algorithm for turbo codes and Low-Density Parity-Check (LDPC) codes. The belief propagation algorithm relies on a graph-based representation of an appropriately chosen factor graph for the recommender systems. The factor graph representation of the recommender systems turned out to be a bipartite graph, where the users and products are arranged as two sets of variable and factor nodes that are connected via some edges. Recommendations (predicted ratings) for each particular user can be computed by probabilistic message passing between nodes in the graph. We provide an evaluation of BPRS via computer simulations using the MovieLens dataset. We observed that BPRS iteratively reduces the error in the predicted ratings of the users until it converges. Further, our initial results indicate an improvement in the Mean Average Error (MAE) and Root Mean Square Error (RMSE) over the Item Averaging. Therefore, we are confident that the belief propagation is a new promising approach which will offer robustness and accuracy for the recommender systems.