With the emergence of various online platforms, associating different platforms is playing an increasingly important role in many applications. Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing methods do not fully exploit high-order nonlinear connectivity information in cross-domain recommendation scenario and suffer from domain-incompatibility problem. In this paper, we propose an end-to-end convolution and fusion model for cross-platform recommendation (CFCR). The proposed CFCR model utilizes Graph Convolution Networks (GCN) to extract user and item features on graphs from different platforms, and fuses cross-platform information by Multimodal AutoEncoder (MAE) with common latent user features. Therefore, the high-order connectivity information is preserved to the most extent and domain-invariant user representations are automatically obtained. The domain-incompatible information is spontaneously discarded to avoid messing up the cross-platform association. Extensive experiments for the proposed CFCR model on real-world dataset demonstrate its advantages over existing cross-platform recommendation methods in terms of various evaluation metrics.

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.

In this demo, we present T2U, a cross-platform video recommendation system with a novel interface. First, we propose a cross-platform content association method using deep neural networks. Based on the proposed deep cross-platform association model, we then propose a cross-platform video recommendation system. Different from existing video websites, T2U bridges user information across social and video platforms and is able to effectively solve data sparsity and cold start problem. In addition, our system also design a novel user-friendly interface. It is designed to show not only the recommended videos but also the reasons to users intuitively, thus significantly enhances user experience.