Filter-enhance temporal graph neural network for continuous-time sequential recommendation

Abstract

Sequential recommendation systems exploit the user's historical item sequences to predict their next actions. Recently, dynamic graph-based methods have been studied and achieved excellent performance for recommendation. They capture dynamic collaborative signals between different user sequences by stacking multiple network layer with attention mechanism to solve insufficient interest mining problem caused by use a single user’s sequence. In online platforms, recorded user behavior data may contain noise, and stacking multiple attention network is easy to aggravate the effects of noise. In this paper, we propose Filter-enhance Temporal Graph Neural Network for Continuous-Time Sequential Recommendation (FTGRec), which connects the related interactions of different user by dynamic graph and design a module combining Fourier transform and attention mechanism to filter the noise data, to predict the order pattern of users better. Empirical results on three datasets indicate FTGRec outperforms other comparative methods.