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

## TL;DR

This paper introduces a novel deep learning model that explicitly accounts for platform-specific disparities and semantic granularity differences to improve cross-platform video recommendation accuracy.

## Contribution

It proposes the Disparity-preserved Deep Cross-platform Association (DCA) model, which effectively captures platform disparities and granularity differences for enhanced recommendation performance.

## Key findings

- DCA significantly outperforms existing methods on real-world datasets.
- The model effectively captures platform-specific information and semantic granularity.
- Experimental results demonstrate improved recommendation accuracy.

## 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.