# GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation

arXiv: https://arxiv.org/abs/2007.02747 · HTML: https://mlsanity.com/paper/2007.02747/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 24 of 24)

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- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
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- [Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation](https://mlsanity.com/paper/2110.03996/) (2021)
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- [Effective and Efficient Training for Sequential Recommendation using Recency Sampling](https://mlsanity.com/paper/2207.02643/) (2022)
- [Continual Text-to-Video Retrieval with Frame Fusion and Task-Aware Routing](https://mlsanity.com/paper/2503.10111/) (2025)
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- [Efficient Graph Neural Network Inference at Large Scale](https://mlsanity.com/paper/2211.00495/) (2022)
- [GPT4Rec: Graph Prompt Tuning for Streaming Recommendation](https://mlsanity.com/paper/2406.08229/) (2024)
- [Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion](https://mlsanity.com/paper/2312.15826/) (2023)
- [Commute Graph Neural Networks](https://mlsanity.com/paper/2407.01635/) (2024)
- [ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender Systems](https://mlsanity.com/paper/2407.13163/) (2024)
- [Lightweight Compositional Embeddings for Incremental Streaming Recommendation](https://mlsanity.com/paper/2202.02427/) (2022)
- [Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems](https://mlsanity.com/paper/2209.01143/) (2022)
- [STAR: A Session-Based Time-Aware Recommender System](https://mlsanity.com/paper/2211.06394/) (2022)
- [Self-supervised Graph-based Point-of-interest Recommendation](https://mlsanity.com/paper/2210.12506/) (2022)
- [DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic Reward](https://mlsanity.com/paper/2505.07257/) (2025)
- [Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching](https://mlsanity.com/paper/2304.03215/) (2023)
- [Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels](https://mlsanity.com/paper/2102.06950/) (2021)
- [Feature Staleness Aware Incremental Learning for CTR Prediction](https://mlsanity.com/paper/2505.02844/) (2025)
- [Residual Dominance as a Structural Account of Last-Item Reliance in Causal Self-Attention Recommenders](https://mlsanity.com/paper/2608.14021/) (2026)

## Abstract (arXiv)

Streaming session-based recommendation (SSR) is a challenging task that requires the recommender system to do the session-based recommendation (SR) in the streaming scenario. In the real-world applications of e-commerce and social media, a sequence of user-item interactions generated within a certain period are grouped as a session, and these sessions consecutively arrive in the form of streams. Most of the recent SR research has focused on the static setting where the training data is first acquired and then used to train a session-based recommender model. They need several epochs of training over the whole dataset, which is infeasible in the streaming setting. Besides, they can hardly well capture long-term user interests because of the neglect or the simple usage of the user information. Although some streaming recommendation strategies have been proposed recently, they are designed for streams of individual interactions rather than streams of sessions. In this paper, we propose a Global Attributed Graph (GAG) neural network model with a Wasserstein reservoir for the SSR problem. On one hand, when a new session arrives, a session graph with a global attribute is constructed based on the current session and its associate user. Thus, the GAG can take both the global attribute and the current session into consideration to learn more comprehensive representations of the session and the user, yielding a better performance in the recommendation. On the other hand, for the adaptation to the streaming session scenario, a Wasserstein reservoir is proposed to help preserve a representative sketch of the historical data. Extensive experiments on two real-world datasets have been conducted to verify the superiority of the GAG model compared with the state-of-the-art methods.