# NISER: Normalized Item and Session Representations to Handle Popularity Bias

arXiv: https://arxiv.org/abs/1909.04276 · HTML: https://mlsanity.com/paper/1909.04276/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 12 of 12)

- [CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space](https://mlsanity.com/paper/2204.11067/) (2022)
- [Session-aware Linear Item-Item Models for Session-based Recommendation](https://mlsanity.com/paper/2103.16104/) (2021)
- [Integrating Large Language Models with Graphical Session-Based Recommendation](https://mlsanity.com/paper/2402.16539/) (2024)
- [Temporal Linear Item-Item Model for Sequential Recommendation](https://mlsanity.com/paper/2412.07382/) (2024)
- [Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation](https://mlsanity.com/paper/2301.03780/) (2023)
- [SR-GCL: Session-Based Recommendation with Global Context Enhanced Augmentation in Contrastive Learning](https://mlsanity.com/paper/2209.10807/) (2022)
- [SimCGNN: Simple Contrastive Graph Neural Network for Session-based Recommendation](https://mlsanity.com/paper/2302.03997/) (2023)
- [Context-aware Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/2310.09593/) (2023)
- [TempGNN: Temporal Graph Neural Networks for Dynamic Session-Based Recommendations](https://mlsanity.com/paper/2310.13249/) (2023)
- [Session-based Recommender Systems: User Interest as a Stochastic Process in the Latent Space](https://mlsanity.com/paper/2504.10005/) (2025)
- [PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems](https://mlsanity.com/paper/2507.19067/) (2025)
- [Bid Farewell to Seesaw: Towards Accurate Long-tail Session-based Recommendation via Dual Constraints of Hybrid Intents](https://mlsanity.com/paper/2511.08378/) (2025)

## Abstract (arXiv)

The goal of session-based recommendation (SR) models is to utilize the information from past actions (e.g. item/product clicks) in a session to recommend items that a user is likely to click next. Recently it has been shown that the sequence of item interactions in a session can be modeled as graph-structured data to better account for complex item transitions. Graph neural networks (GNNs) can learn useful representations for such session-graphs, and have been shown to improve over sequential models such as recurrent neural networks [14]. However, we note that these GNN-based recommendation models suffer from popularity bias: the models are biased towards recommending popular items, and fail to recommend relevant long-tail items (less popular or less frequent items). Therefore, these models perform poorly for the less popular new items arriving daily in a practical online setting. We demonstrate that this issue is, in part, related to the magnitude or norm of the learned item and session-graph representations (embedding vectors). We propose a training procedure that mitigates this issue by using normalized representations. The models using normalized item and session-graph representations perform significantly better: i. for the less popular long-tail items in the offline setting, and ii. for the less popular newly introduced items in the online setting. Furthermore, our approach significantly improves upon existing state-of-the-art on three benchmark datasets.