Cross-domain recommendation without shared users or items by sharing latent vector distributions

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Tomoharu Iwata, Takeuchi Koh ;
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, PMLR 38:379-387, 2015.

Abstract

We propose a cross-domain recommendation method for predicting the ratings of items in different domains, where neither users nor items are shared across domains. The proposed method is based on matrix factorization, which learns a latent vector for each user and each item. Matrix factorization techniques for a single-domain fail in the cross-domain recommendation task because the learned latent vectors are not aligned over different domains. The proposed method assumes that latent vectors in different domains are generated from a common Gaussian distribution with a full covariance matrix. By inferring the mean and covariance of the common Gaussian from given cross-domain rating matrices, the latent factors are aligned, which enables us to predict ratings in different domains. Experiments conducted on rating datasets from a wide variety of domains, e.g., movie, books and electronics, demonstrate that the proposed method achieves higher performance for predicting cross-domain ratings than existing methods.

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