Context-Dependent Propagating-Based Video Recommendation in Multimodal Heterogeneous Information Networks

With the emergence of online social networks (OSNs), video recommendation has come to play a crucial role in mitigating the semantic gap between users and videos. Conventional approaches to video recommendation primarily focus on exploiting content features or simple user-video interactions to model the users' preferences. Although these methods have achieved promising results, they fail to model the complex video context interdependency, which is obscure/hidden in heterogeneous auxiliary data from OSNs. In this paper, we study the problem of video recommendation in Heterogeneous Information Networks (HINs) due to its excellence in characterizing heterogeneous and complex context information. We propose a Context-Dependent Propagating Recommendation network (CDPRec) to obtain accurate video embedding and capture global context cues among videos in HINs. The CDPRec can iteratively propagate the contexts of a video along links in a graph-structured HIN and explore multiple types of dependencies among the surrounding video nodes. Then, each video is represented as the composition of the multimodal content feature and global dependency structure information using an attention network. The learned video embedding with sequential based recommendation are jointly optimized for the final rating prediction. Experimental results on real-world YouTube video recommendation scenarios demonstrate the effectiveness of the proposed methods compared with strong baselines.