Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer
Abstract
The Cross Domain Collaborative Filtering (CDCF) exploits the rating matrices from multiple domains to make better recommendations. Existing CDCF methods adopt the sub-structure sharing technique that can only transfer linearly correlated knowledge between domains. In this paper, we propose the notion of Hyper-Structure Transfer (HST) that requires the rating matrices to be explained by the projections of some more complex structure, called the hyper-structure, shared by all domains, and thus allows the non-linearly correlated knowledge between domains to be identified and transferred. Extensive experiments are conducted and the results demonstrate the effectiveness of our HST models empirically.
Cite
Text
Liu et al. "Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer." International Conference on Machine Learning, 2015.Markdown
[Liu et al. "Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer." International Conference on Machine Learning, 2015.](https://mlanthology.org/icml/2015/liu2015icml-nonlinear/)BibTeX
@inproceedings{liu2015icml-nonlinear,
title = {{Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer}},
author = {Liu, Yan-Fu and Hsu, Cheng-Yu and Wu, Shan-Hung},
booktitle = {International Conference on Machine Learning},
year = {2015},
pages = {1190-1198},
volume = {37},
url = {https://mlanthology.org/icml/2015/liu2015icml-nonlinear/}
}