Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Guest Editor's Introduction: Information Customization
IEEE Intelligent Systems
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Neural Computation
Adaptive mixtures of local experts
Neural Computation
UAI'01 Proceedings of the Seventeenth 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
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We consider techniques based on artificial neural networks for combining collaborative (social) and content information in a recommender system in order to enhance recommendation performance. We find that the recommendation quality achieved by a feedforward multilayer perceptron network operating on combined collaborative and content-based information (preprocessed using the singular value decomposition) is statistically significantly better than that of a network that is provided with the collaborative data alone, assuming that dimensionality reduction is performed on the collaborative and content-based data components separately. We propose a mixture of attribute experts neural network architecture that exploits the natural division between content and social information in order to reduce the number of network connections, resulting in more efficient training and recommendation than a standard fully connected network. We characterize the set of functions that can be expressed by mixture of attribute experts networks. The top 3 precision achieved by a recommender system based on our mixture of attribute experts architecture is superior to that of a purely collaborative system at a strong statistical significance level (P 0.01). A random restarting technique reduces the average running time without affecting recommendation precision. CR Categories and Subject Descriptors. H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval - Information Filtering; H.2.8 [Database Management]: Database Applications - Data Mining; 1.2.6 [Artificial Intelligence]: Learning - Connectionism and neural nets; 1.5.1 [Pattern Recognition]: Models - Neural nets; 1.5.5 [Pattern Recognition I: Implementation - Special architectures.