[KDD 2020] Gemini: A novel and universal heterogeneous graph information fusing framework for online recommendations - CrossMinds.ai
[KDD 2020] Gemini: A novel and universal heterogeneous graph information fusing framework for online recommendations
Aug 13, 202014 views
Jixing Xu
Recently, network embedding has been successfully used in recommendation systems. Researchers have made efforts to utilize,additional auxiliary information (e.g., social relations of users) to,improve performance. However, such auxiliary information lacks,compatibility for all recommendation scenarios, thus it is difficult,to apply in some industrial scenarios where generality is required.,Moreover, the heterogeneous nature between users and items aggravates the difficulty in network information fusion. Many works,tried to transform user-item heterogeneous network to two homogeneous graphs (i.e., user-user and item-item), and then fuse,information separately. This may limit the representation power of,learned embedding due to ignoring the adjacent relationship in the,original graph. In addition, the sparsity of user-item interactions is,an urgent problem need to be solved.,To solve the above problems, we propose a universal and effective framework named Gemini, which only relies on the common,interaction logs, avoiding the dependence on auxiliary information,and ensuring a better generality. For the purpose of keeping original adjacent relationship, Gemini transforms the original user-item,heterogeneous graph into two semi homogeneous graphs from the,perspective of users and items respectively. The transformed graphs,consist of two types of nodes: network nodes coming from homogeneous nodes and attribute nodes coming from heterogeneous,node. Then, the node representation is learned in a homogeneous,way, with considering edge embedding at the same time. Simultaneously, the interaction sparsity problem is solved to some extent,as the transformed graphs contain the original second-order neighbors. For training efficiently, we also propose an iterative training,algorithm to reduce computational complexity. Experimental results on the five datasets and online A/B tests in recommendations,of DiDiChuXing show that Gemini outperforms state-of-the-art,algorithms.