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arXiv · 1901.00171

Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation

Shengze Yu·Xin Wang·Wenwu Zhu·Peng Cui·Jingdong Wang
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Abstract

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.

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Shengze Yu, Xin Wang, Wenwu Zhu, Peng Cui, Jingdong Wang. 2019-01-01. Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation. https://doi.org/10.24963/ijcai.2019%2F644

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