# Introducing Self-Attention to Target Attentive Graph Neural Networks

arXiv: https://arxiv.org/abs/2107.01516 · HTML: https://mlsanity.com/paper/2107.01516/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 19 references that are in the map.
- First posted on arXiv 4 Jul 2021.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Are Transformers universal approximators of sequence-to-sequence functions?](https://mlsanity.com/paper/1912.10077/) (2019)
- [High-Performance Large-Scale Image Recognition Without Normalization](https://mlsanity.com/paper/2102.06171/) (2021)
- [Why gradient clipping accelerates training: A theoretical justification for adaptivity](https://mlsanity.com/paper/1905.11881/) (2019)
- [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)
- [Generalization and Representational Limits of Graph Neural Networks](https://mlsanity.com/paper/2002.06157/) (2020)
- [A Survey on Session-based Recommender Systems](https://mlsanity.com/paper/1902.04864/) (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)
- [TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2005.02844/) (2020)
- [AutoClip: Adaptive Gradient Clipping for Source Separation Networks](https://mlsanity.com/paper/2007.14469/) (2020)
- [DGTN: Dual-channel Graph Transition Network for Session-based Recommendation](https://mlsanity.com/paper/2009.10002/) (2020)

## Built on by (most cited first, 4 of 4)

- [Graph Machine Learning in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2404.14928/) (2024)
- [Holder Recommendations using Graph Representation Learning & Link Prediction](https://mlsanity.com/paper/2212.09624/) (2022)
- [A GNN Model with Adaptive Weights for Session-Based Recommendation Systems](https://mlsanity.com/paper/2408.05051/) (2024)
- [Session-based Recommender Systems: User Interest as a Stochastic Process in the Latent Space](https://mlsanity.com/paper/2504.10005/) (2025)

## Abstract (arXiv)

Session-based recommendation systems suggest relevant items to users by modeling user behavior and preferences using short-term anonymous sessions. Existing methods leverage Graph Neural Networks (GNNs) that propagate and aggregate information from neighboring nodes i.e., local message passing. Such graph-based architectures have representational limits, as a single sub-graph is susceptible to overfit the sequential dependencies instead of accounting for complex transitions between items in different sessions. We propose a new technique that leverages a Transformer in combination with a target attentive GNN. This allows richer representations to be learnt, which translates to empirical performance gains in comparison to a vanilla target attentive GNN. Our experimental results and ablation show that our proposed method is competitive with the existing methods on real-world benchmark datasets, improving on graph-based hypotheses. Code is available at https://github.com/The-Learning-Machines/SBR