Abstract Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing crossplatform 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-specifific 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 Crossplatform Association (DCA), taking platformspecifific disparity and granularity difference into consideration. The proposed DCA model employs a partially-connected multi-modal autoencoder, which is capable of explicitly capturing platform-specifific information, as well as utilizing nonlinear mapping functions to handle granularity differences. We then present a crossplatform 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 signifificantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics