  Cross-platform recommendation aims to improve recommendation accuracy through
associating information from different platforms. Existing cross-platform
recommendation approaches assume all cross-platform information to be
consistent with each other and can be aligned. However, there remain two
unsolved challenges: i) there exist inconsistencies in cross-platform
association due to platform-specific disparity, and ii) data from distinct
platforms may have different semantic granularities. In this paper, we propose
a cross-platform association model for cross-platform video recommendation,
i.e., Disparity-preserved Deep Cross-platform Association (DCA), taking
platform-specific disparity and granularity difference into consideration. The
proposed DCA model employs a partially-connected multi-modal autoencoder, which
is capable of explicitly capturing platform-specific information, as well as
utilizing nonlinear mapping functions to handle granularity differences. We
then present a cross-platform video recommendation approach based on the
proposed DCA model. Extensive experiments for our cross-platform recommendation
framework on real-world dataset demonstrate that the proposed DCA model
significantly outperform existing cross-platform recommendation methods in
terms of various evaluation metrics.
