# Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

arXiv: https://arxiv.org/abs/2107.03813 · HTML: https://mlsanity.com/paper/2107.03813/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 23 of 23)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings](https://mlsanity.com/paper/2006.13009/) (2020)
- [A Survey on Session-based Recommender Systems](https://mlsanity.com/paper/1902.04864/) (2019)
- [Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks](https://mlsanity.com/paper/1706.04148/) (2017)
- [Global Context Enhanced Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2106.05081/) (2021)
- [Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation](https://mlsanity.com/paper/2012.06852/) (2020)
- [Interactive Path Reasoning on Graph for Conversational Recommendation](https://mlsanity.com/paper/2007.00194/) (2020)
- [Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation](https://mlsanity.com/paper/1908.04942/) (2019)
- [RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation](https://mlsanity.com/paper/1812.02646/) (2018)
- [Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1911.11942/) (2019)
- [A Survey on Trajectory Data Management, Analytics, and Learning](https://mlsanity.com/paper/2003.11547/) (2020)
- [Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks](https://mlsanity.com/paper/1804.00823/) (2018)
- [Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems](https://mlsanity.com/paper/1904.04381/) (2019)
- [Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction](https://mlsanity.com/paper/2002.07993/) (2020)
- [Session-aware Recommendation: A Surprising Quest for the State-of-the-art](https://mlsanity.com/paper/2011.03424/) (2020)

## Built on by (most cited first, 22 of 22)

- [Homophily-oriented Heterogeneous Graph Rewiring](https://mlsanity.com/paper/2302.06299/) (2023)
- [Poisoning Federated Recommender Systems with Fake Users](https://mlsanity.com/paper/2402.11637/) (2024)
- [Disentangling ID and Modality Effects for Session-based Recommendation](https://mlsanity.com/paper/2404.12969/) (2024)
- [Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation](https://mlsanity.com/paper/2301.03780/) (2023)
- [Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph](https://mlsanity.com/paper/2305.10771/) (2023)
- [HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning](https://mlsanity.com/paper/2109.02868/) (2021)
- [Everything Perturbed All at Once: Enabling Differentiable Graph Attacks](https://mlsanity.com/paper/2308.15614/) (2023)
- [Sequential Search with Off-Policy Reinforcement Learning](https://mlsanity.com/paper/2202.00245/) (2022)
- [Tri-Attention: Explicit Context-Aware Attention Mechanism for Natural Language Processing](https://mlsanity.com/paper/2211.02899/) (2022)
- [Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation](https://mlsanity.com/paper/2311.09577/) (2023)
- [Performance Comparison of Session-based Recommendation Algorithms based on GNNs](https://mlsanity.com/paper/2312.16695/) (2023)
- [BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation](https://mlsanity.com/paper/2402.12733/) (2024)
- [Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning](https://mlsanity.com/paper/2407.00082/) (2024)
- [Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation](https://mlsanity.com/paper/2507.04623/) (2025)
- [Quaternion-Based Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2111.10536/) (2021)
- [Heterogeneous Graph Neural Network for Personalized Session-Based Recommendation with User-Session Constraints](https://mlsanity.com/paper/2205.11343/) (2022)
- [Toward Robust Recommendation via Real-time Vicinal Defense](https://mlsanity.com/paper/2309.17278/) (2023)
- [LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs](https://mlsanity.com/paper/2409.06323/) (2024)
- [CUPID: A Real-Time Session-Based Reciprocal Recommendation System for a One-on-One Social Discovery Platform](https://mlsanity.com/paper/2410.18087/) (2024)
- [Heterogeneous Graph Backdoor Attack](https://mlsanity.com/paper/2506.00191/) (2025)
- [A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges](https://mlsanity.com/paper/2607.16198/) (2026)
- [Efficient Recommendations via Graph Coarsening and Label Propagation](https://mlsanity.com/paper/2607.22287/) (2026)

## Abstract (arXiv)

Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition patterns, and neglect the impact of user historical sessions while modeling user preference, which often leads to non-personalized recommendation. Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions. To address these issues, we propose a novel Heterogeneous Global Graph Neural Networks (HG-GNN) to exploit the item transitions over all sessions in a subtle manner for better inferring user preference from the current and historical sessions. To effectively exploit the item transitions over all sessions from users, we propose a novel heterogeneous global graph that contains item transitions of sessions, user-item interactions and global co-occurrence items. Moreover, to capture user preference from sessions comprehensively, we propose to learn two levels of user representations from the global graph via two graph augmented preference encoders. Specifically, we design a novel heterogeneous graph neural network (HGNN) on the heterogeneous global graph to learn the long-term user preference and item representations with rich semantics. Based on the HGNN, we propose the Current Preference Encoder and the Historical Preference Encoder to capture the different levels of user preference from the current and historical sessions, respectively. To achieve personalized recommendation, we integrate the representations of the user current preference and historical interests to generate the final user preference representation. Extensive experimental results on three real-world datasets show that our model outperforms other state-of-the-art methods.