Associative neural memories
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
SNACK: incorporating social network information in automated collaborative filtering
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Computers
Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Active collaborative filtering
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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This paper introduces a collaborative filtering (CF) neural-network algorithm for recommending items. This algorithm connects the study of collaborative filtering with the study of associative memory, which is a neural network architecture that is significantly different from the dominant feedforward design. There are two types of CF systems – user-based and item-based, and we show that our CF system can have both interpretations. We further prove that, given a random subset of all users, our CF system is an unbiased estimator of predictions made from all users, thus theoretically justifying random sampling. We further apply standard neural network techniques, such as magnitude pruning and principle component analysis, to improve the system's scalability. Results from experiments with the MovieLens dataset are shown.