Qinyong Wang

TL;DR: Coder et al. as discussed by the authors proposed a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations, where each channel in the network encodes a hypergraph that depicts a common highorder user relation pattern via hypergraph CNN.

TL;DR: An adaptive negative sampling framework based on Generative Adversarial Nets (GAN) is developed to optimize the proposed streaming recommender model, which effectively overcomes the limitations of classical negative sampling approaches and improves both effectiveness and efficiency of the model parameter inference.

TL;DR: This paper models session-based data as a hypergraph and proposes a dual channel hypergraph convolutional network -- DHCN to improve SBR and innovatively integrates self-supervised learning into the training of the network by maximizing mutual information between the session representations learned via the two channels in DHCn.

TL;DR: A novel group recommender system, namely SIGR (short for "Social Influence-based Group Recommender"), which takes an attention mechanism and a bipartite graph embedding model BGEM as building blocks and develops a novel deep social influence learning framework to exploit and integrate users' global and local social network structure information to further improve the estimation of users' social influences.

TL;DR: Xia et al. as discussed by the authors proposed a dual channel hypergraph convolutional network (DHCN) to model session-based data as a hypergraph and integrate self-supervised learning into the training of the networks.