# DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation

arXiv: https://arxiv.org/abs/2105.14428 · HTML: https://mlsanity.com/paper/2105.14428/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2021.

## Built on by (most cited first, 3 of 3)

- [Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again](https://mlsanity.com/paper/2210.08122/) (2022)
- [All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks](https://mlsanity.com/paper/2407.14996/) (2024)
- [Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication](https://mlsanity.com/paper/2306.10466/) (2023)

## Abstract (arXiv)

Session-based recommendations have been widely adopted for various online video and E-commerce Websites. Most existing approaches are intuitively proposed to discover underlying interests or preferences out of the anonymous session data. This apparently ignores the fact these sequential behaviors usually reflect session user's potential demand, i.e., a semantic level factor, and therefore how to estimate underlying demands from a session is challenging. To address aforementioned issue, this paper proposes a demand-aware graph neural networks (DAGNN). Particularly, a demand modeling component is designed to first extract session demand and the underlying multiple demands of each session is estimated using the global demand matrix. Then, the demand-aware graph neural network is designed to extract session demand graph to learn the demand-aware item embedddings for the later recommendations. The mutual information loss is further designed to enhance the quality of the learnt embeddings. Extensive experiments are evaluated on several real-world datasets and the proposed model achieves the SOTA model performance.