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

Qi Zhan; Shanhong Zheng; Chuang Chen; Xingchen Du

doi:10.1117/12.2681024

23 May 2023 Filter-enhance temporal graph neural network for continuous-time sequential recommendation

Qi Zhan, Shanhong Zheng, Chuang Chen, Xingchen Du

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126452R (2023) https://doi.org/10.1117/12.2681024
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

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.

Citation Download Citation

Qi Zhan, Shanhong Zheng, Chuang Chen, and Xingchen Du "Filter-enhance temporal graph neural network for continuous-time sequential recommendation", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126452R (23 May 2023); https://doi.org/10.1117/12.2681024

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