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

author: Shan-Hung Wu, Department of Computer Science, National Tsing Hua University
published: Sept. 27, 2015,   recorded: July 2015,   views: 1728

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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.

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