Heterogeneous Sequel-Aware Graph Neural Networks for Sequential Learning

TL;DR

This paper introduces sequel-aware graph neural networks that incorporate temporal item sequences to improve recommendation accuracy, demonstrating superior performance over existing methods on multiple datasets.

Contribution

The paper proposes a novel heterogenous sequel-aware GNN model that effectively integrates temporal item sequences for enhanced sequential learning in recommendation systems.

Findings

Sequel-aware GNNs outperform traditional models in recommendation tasks.

Incorporating sequence information significantly improves recommendation quality.

Extensive experiments validate the effectiveness of the proposed approach.

Abstract

Graph-based recommendation systems use higher-order user and item embeddings for next-item predictions. Dynamically adding collaborative signals from neighbors helps to use similar users' preferences during learning. While item-item correlations and their impact on recommendations have been studied, the efficacy of temporal item sequences for recommendations is much less explored. In this paper, we examine temporal item sequence (sequel-aware) embeddings along with higher-order user embeddings and show that sequel-aware Graph Neural Networks have better (or comparable) recommendation performance than graph-based recommendation systems that do not consider sequel information. Extensive empirical results comparing Heterogeneous Sequel-aware Graph Neural Networks (HSAL-GNNs) to other algorithms for sequential learning (such as transformers, graph neural networks, auto-encoders) are presented on three synthetic and three real-world datasets. Our results indicate that the incorporation of sequence information from items greatly enhances recommendations.