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

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v38-iwata15, title = {{Cross-domain recommendation without shared users or items by sharing latent vector distributions}}, author = {Iwata, Tomoharu and Koh, Takeuchi}, booktitle = {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics}, pages = {379--387}, year = {2015}, editor = {Lebanon, Guy and Vishwanathan, S. V. N.}, volume = {38}, series = {Proceedings of Machine Learning Research}, address = {San Diego, California, USA}, month = {09--12 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v38/iwata15.pdf}, url = {https://proceedings.mlr.press/v38/iwata15.html}, 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.} }
Endnote
%0 Conference Paper %T Cross-domain recommendation without shared users or items by sharing latent vector distributions %A Tomoharu Iwata %A Takeuchi Koh %B Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2015 %E Guy Lebanon %E S. V. N. Vishwanathan %F pmlr-v38-iwata15 %I PMLR %P 379--387 %U https://proceedings.mlr.press/v38/iwata15.html %V 38 %X 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.
RIS
TY - CPAPER TI - Cross-domain recommendation without shared users or items by sharing latent vector distributions AU - Tomoharu Iwata AU - Takeuchi Koh BT - Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics DA - 2015/02/21 ED - Guy Lebanon ED - S. V. N. Vishwanathan ID - pmlr-v38-iwata15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 38 SP - 379 EP - 387 L1 - http://proceedings.mlr.press/v38/iwata15.pdf UR - https://proceedings.mlr.press/v38/iwata15.html AB - 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. ER -
APA
Iwata, T. & Koh, T.. (2015). Cross-domain recommendation without shared users or items by sharing latent vector distributions. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 38:379-387 Available from https://proceedings.mlr.press/v38/iwata15.html.

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