# TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation

arXiv: https://arxiv.org/abs/2005.02844 · HTML: https://mlsanity.com/paper/2005.02844/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 30 of 33)

- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [Mining Latent Structures for Multimedia Recommendation](https://mlsanity.com/paper/2104.09036/) (2021)
- [Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation](https://mlsanity.com/paper/2307.09688/) (2023)
- [CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space](https://mlsanity.com/paper/2204.11067/) (2022)
- [Dynamic Graph Collaborative Filtering](https://mlsanity.com/paper/2101.02844/) (2021)
- [Large Language Models for Intent-Driven Session Recommendations](https://mlsanity.com/paper/2312.07552/) (2023)
- [Dynamic Graph Representation Learning with Neural Networks: A Survey](https://mlsanity.com/paper/2304.05729/) (2023)
- [Relation-aware Heterogeneous Graph for User Profiling](https://mlsanity.com/paper/2110.07181/) (2021)
- [M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations](https://mlsanity.com/paper/2209.11824/) (2022)
- [Adaptive In-Context Learning with Large Language Models for Bundle Generation](https://mlsanity.com/paper/2312.16262/) (2023)
- [Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation](https://mlsanity.com/paper/2403.16427/) (2024)
- [GraphPAS: Parallel Architecture Search for Graph Neural Networks](https://mlsanity.com/paper/2112.03461/) (2021)
- [Implicit Session Contexts for Next-Item Recommendations](https://mlsanity.com/paper/2208.09076/) (2022)
- [Introducing Self-Attention to Target Attentive Graph Neural Networks](https://mlsanity.com/paper/2107.01516/) (2021)
- [Dynamic Sequential Graph Learning for Click-Through Rate Prediction](https://mlsanity.com/paper/2109.12541/) (2021)
- [Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation](https://mlsanity.com/paper/2301.03780/) (2023)
- [Multi-view Intent Learning and Alignment with Large Language Models for Session-based Recommendation](https://mlsanity.com/paper/2402.13840/) (2024)
- [Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems](https://mlsanity.com/paper/2210.12940/) (2022)
- [Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix](https://mlsanity.com/paper/2307.02126/) (2023)
- [Modeling Multi-Destination Trips with Sketch-Based Model](https://mlsanity.com/paper/2102.11252/) (2021)
- [Graph Spring Network and Informative Anchor Selection for Session-based Recommendation](https://mlsanity.com/paper/2202.09502/) (2022)
- [Hierarchical Multi-Task Learning Framework for Session-based Recommendations](https://mlsanity.com/paper/2309.06533/) (2023)
- [Performance Comparison of Session-based Recommendation Algorithms based on GNNs](https://mlsanity.com/paper/2312.16695/) (2023)
- [Item Cluster-aware Prompt Learning for Session-based Recommendation](https://mlsanity.com/paper/2410.04756/) (2024)
- [Guided Diffusion-based Counterfactual Augmentation for Robust Session-based Recommendation](https://mlsanity.com/paper/2410.21892/) (2024)
- [What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context](https://mlsanity.com/paper/2506.02261/) (2025)
- [Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation](https://mlsanity.com/paper/2507.04623/) (2025)
- [SR-HetGNN:Session-based Recommendation with Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2108.05641/) (2021)
- [SimCGNN: Simple Contrastive Graph Neural Network for Session-based Recommendation](https://mlsanity.com/paper/2302.03997/) (2023)
- [A Simple Yet Effective Approach for Diversified Session-Based Recommendation](https://mlsanity.com/paper/2404.00261/) (2024)

## Abstract (arXiv)

Session-based recommendation nowadays plays a vital role in many websites, which aims to predict users' actions based on anonymous sessions. There have emerged many studies that model a session as a sequence or a graph via investigating temporal transitions of items in a session. However, these methods compress a session into one fixed representation vector without considering the target items to be predicted. The fixed vector will restrict the representation ability of the recommender model, considering the diversity of target items and users' interests. In this paper, we propose a novel target attentive graph neural network (TAGNN) model for session-based recommendation. In TAGNN, target-aware attention adaptively activates different user interests with respect to varied target items. The learned interest representation vector varies with different target items, greatly improving the expressiveness of the model. Moreover, TAGNN harnesses the power of graph neural networks to capture rich item transitions in sessions. Comprehensive experiments conducted on real-world datasets demonstrate its superiority over state-of-the-art methods.