Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation

by   Jiayan Guo, et al.
Peking University
The Hong Kong University of Science and Technology

Session-based recommendation (SBR) aims to predict the user next action based on the ongoing sessions. Recently, there has been an increasing interest in modeling the user preference evolution to capture the fine-grained user interests. While latent user preferences behind the sessions drift continuously over time, most existing approaches still model the temporal session data in discrete state spaces, which are incapable of capturing the fine-grained preference evolution and result in sub-optimal solutions. To this end, we propose Graph Nested GRU ordinary differential equation (ODE), namely GNG-ODE, a novel continuum model that extends the idea of neural ODEs to continuous-time temporal session graphs. The proposed model preserves the continuous nature of dynamic user preferences, encoding both temporal and structural patterns of item transitions into continuous-time dynamic embeddings. As the existing ODE solvers do not consider graph structure change and thus cannot be directly applied to the dynamic graph, we propose a time alignment technique, called t-Alignment, to align the updating time steps of the temporal session graphs within a batch. Empirical results on three benchmark datasets show that GNG-ODE significantly outperforms other baselines.


page 1

page 2

page 3

page 4


Learning Graph ODE for Continuous-Time Sequential Recommendation

Sequential recommendation aims at understanding user preference by captu...

Exploring Global Information for Session-based Recommendation

Session-based recommendation (SBR) is a challenging task, which aims at ...

PEN4Rec: Preference Evolution Networks for Session-based Recommendation

Session-based recommendation aims to predict user the next action based ...

UBER-GNN: A User-Based Embeddings Recommendation based on Graph Neural Networks

The problem of session-based recommendation aims to predict user next ac...

Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer

In order to model the evolution of user preference, we should learn user...

STAR: A Session-Based Time-Aware Recommender System

Session-Based Recommenders (SBRs) aim to predict users' next preferences...

Modelling Evolutionary and Stationary User Preferences for Temporal Sets Prediction

Given a sequence of sets, where each set is associated with a timestamp ...

Please sign up or login with your details

Forgot password? Click here to reset