Heterogeneous Sequel-Aware Graph Neural Networks for Sequential Learning

2025/06/05 by Anushka Tiwari, Tiwari, Anushka, Haimonti Dutta +2
Computer Science · #Advanced Graph Neural Networks #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning in Healthcare #Recommender Systems and Techniques

paper · pdf · doi:10.48550/arxiv.2506.05625

openalex publication_date 2025/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• DiffuRec: A Diffusion Model for Sequential Recommendation
• DiffuRec: A Diffusion Model for Sequential Recommendation
• Simple and Efficient Heterogeneous Graph Neural Network
• Sequential Recommendation via Stochastic Self-Attention
• Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation
• Dynamic Graph Neural Networks for Sequential Recommendation
• Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation
• Fine-Grained Spoiler Detection from Large-Scale Review Corpora
• BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
• Self-Attentive Sequential Recommendation
• Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
• Session-based Recommendations with Recurrent Neural Networks
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• An MDP-based Recommender System
• The Graph Neural Network Model

Related

• Dynamic Graph Neural Networks for Sequential Recommendation
• Memory Augmented Graph Neural Networks for Sequential Recommendation
• End-to-End Graph-Sequential Representation Learning for Accurate Recommendations
• Inter-sequence Enhanced Framework for Personalized Sequential Recommendation
• Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations