Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
The Journal of Machine Learning Research
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Collaborative Filtering Using a Regression-Based Approach
Knowledge and Information Systems
IEEE Transactions on Knowledge and Data Engineering
Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering using orthogonal nonnegative matrix tri-factorization
Information Processing and Management: an International Journal
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
TagRec: Leveraging Tagging Wisdom for Recommendation
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
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Nowadays, products are increasingly abundant and diverse, which makes user more fastidious. In fact, user has demands on a product in many aspects. A user is satisfied with a product usually because he or she likes all aspects of the product. Even only few of his or her demands or interests did not be satisfied, the user will have a bad opinion on the product. Usually, user's rating value for an item can be divided into two parts. One is influenced by his or her rating bias and other user's rating for the item. The other is determined by his or her real opinion on the item. The process of rating an item can be considered as an expression of user's psychological behavior. Based on this rating psychology, a novel collaborative filtering algorithm is proposed. In this algorithm, if one latent demand of the user is not satisfied by the item, the corresponding rating value will be multiplied by a penalty value which is less than 1. The parameters in the model are estimated using stochastic gradient descent method. Experiment results show that this algorithm has better performance than state-of-the-art algorithms.