Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer

@InProceedings{pmlr-v37-liua15,
  title = 	 {Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer},
  author = 	 {Liu, Yan-Fu and Hsu, Cheng-Yu and Wu, Shan-Hung},
  booktitle = 	 {Proceedings of the 32nd International Conference on Machine Learning},
  pages = 	 {1190--1198},
  year = 	 {2015},
  editor = 	 {Bach, Francis and Blei, David},
  volume = 	 {37},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Lille, France},
  month = 	 {07--09 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v37/liua15.pdf},
  url = 	 {https://proceedings.mlr.press/v37/liua15.html},
  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.}
}