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
Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
User's latent interest-based collaborative filtering
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
User preference representation based on psychometric models
ADC '11 Proceedings of the Twenty-Second Australasian Database Conference - Volume 115
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In this paper, the psychometrics model, i.e. the rating scale model, is extended from one dimension to multiple dimension. Then, based on this, a novel collaborative filtering algorithm is proposed. In this algorithm, user's interest and item's quality are represented by vectors. User's rating for an item is a weighted summation of the user's latent ratings for the item in all dimensions, in which the weights are user-specific. Moreover, user's latent rating in each dimension is assumed to follow a multinomial distribution that is determined by the user's interest value, the item's quality value in this dimension, and the thresholds between two consecutive ratings. The parameters are estimated by minimizing the loss function using the stochastic gradient descent method. Experimental results on the benchmark data sets show the superiority of our algorithm.