Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation
This work addresses cross-platform recommendation challenges for video platforms, but it appears incremental as it builds on existing cross-platform approaches by adding disparity and granularity handling.
The paper tackles the problem of cross-platform video recommendation by addressing inconsistencies and semantic granularity differences between platforms, proposing a Disparity-preserved Deep Cross-platform Association (DCA) model that significantly outperforms existing methods in experiments on a real-world dataset.
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.