Adaptive Context-Embedded Hypergraph Convolutional Network for Session-based Recommendation

Abstract

The graph neural network (GNN) based approaches have attracted more and more attention in session-based recommendation tasks. However, most of the existing methods do not fully take advantage of context information in session when capturing user’s interest, and the research on context adaptation is even less. Furthermore, hypergraph has potential to express complex relations among items, but it has remained unexplored. Therefore, this paper proposes an adaptive context-embedded hypergraph convolutional network (AC-HCN) for session-based recommendation. At first, the data of sessions is constructed as session hypergraph. Then, the representation of each item in session hypergraph is learned using an adaptive context-embedded hypergraph convolution. In the convolution, different types of context information from both current item itself and the item’s neighborhoods are adaptively integrated into the representation updating of current item. Meanwhile, an adaptive transformation function is employed to effectively eliminate the effects of irrelevant items. Then, the learned item representations are combined with time interval embeddings and reversed position embeddings to fully reflect time interval information and sequential information between items in session. Finally, based on learned item representations in session, a soft attention mechanism is used to obtain user’s interest, and then a recommendation list is given. Extensive experiments on the real-world datasets show that the proposed model has significantly improvement compared with the state-of-arts methods.

A Knowledge-Graph-Driven Method for Intelligent Decision Making on Power Communication Equipment Faults

“…Recommender systems are based on historical interaction data, mining user preferences and then recommending them to users [30,31]. Most of the early recommendation methods used user-personalized set matching based on collaborative filtering matching methods [32][33][34].…”