Time enhanced graph neural networks for session-based recommendation

Abstract

Session-based recommendation (SBR) is a challenging task, aiming at recommending items according to the behavior of anonymous users. Previous research efforts mainly focus on capturing sequential transitions between consecutive items via recurrent neural networks (RNN) or modeling the complex transitions between non-adjacent items based on graph neural networks (GNN). Although these works have achieved encouraging performance on solving the session-based recommendation problem, few efforts have been dedicated to exploring the rich information related to the shifts of user interests within the transition relationships, which is the research gap we attempt to bridge in this work. In this paper, we propose a novel model, named Time Enhanced Graph Neural Networks (TE-GNN), which attempts to capture the complex user interest shift patterns within sessions. In TE-GNN, we construct a Time Enhanced Session Graph (TES-Graph) where transition relationships between items are treated adaptively with respect to the degree of user interest drift. In addition, a novel Temporal Graph Convolutional Network (T-GCN) is designed to learn item embeddings based on the TES-Graph. Moreover, we also introduce a Temporal Interest Attention Network (TIAN) to model the complex transition of items with a common user interest. Extensive experiments have been conducted on four widely used benchmark datasets, i.e., Diginetica, Tmall, Nowplaying, and Retailrocket, and the results show that our proposed approach TE-GNN significantly outperforms previous state-of-the-art baseline methods. The implementation of TE-GNN is available in https://github.com/GuTang1997/TE-GNN.