# Session-based Recommender Systems: User Interest as a Stochastic Process in the Latent Space

arXiv: https://arxiv.org/abs/2504.10005 · HTML: https://mlsanity.com/paper/2504.10005/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 33 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 22 of 22)

- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Self-supervised Graph Learning for Recommendation](https://mlsanity.com/paper/2010.10783/) (2020)
- [GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks](https://mlsanity.com/paper/2103.08826/) (2021)
- [A Survey on Session-based Recommender Systems](https://mlsanity.com/paper/1902.04864/) (2019)
- [Improved Recurrent Neural Networks for Session-based Recommendations](https://mlsanity.com/paper/1606.08117/) (2016)
- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems](https://mlsanity.com/paper/2005.12964/) (2020)
- [TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2005.02844/) (2020)
- [NISER: Normalized Item and Session Representations to Handle Popularity Bias](https://mlsanity.com/paper/1909.04276/) (2019)
- [Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty](https://mlsanity.com/paper/2006.10932/) (2020)
- [Uncertainty Quantification for Fairness in Two-Stage Recommender Systems](https://mlsanity.com/paper/2205.15436/) (2022)
- [Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation](https://mlsanity.com/paper/2209.11461/) (2022)
- [Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems](https://mlsanity.com/paper/2404.18640/) (2024)
- [Introducing Self-Attention to Target Attentive Graph Neural Networks](https://mlsanity.com/paper/2107.01516/) (2021)
- [Popularity Debiasing from Exposure to Interaction in Collaborative Filtering](https://mlsanity.com/paper/2305.05204/) (2023)
- [Graph-based Alignment and Uniformity for Recommendation](https://mlsanity.com/paper/2308.09292/) (2023)
- [STAR: A Session-Based Time-Aware Recommender System](https://mlsanity.com/paper/2211.06394/) (2022)
- [SR-PredictAO: Session-based Recommendation with High-Capability Predictor Add-On](https://mlsanity.com/paper/2309.12218/) (2023)
- [Estimating Propensity for Causality-based Recommendation without Exposure Data](https://mlsanity.com/paper/2310.20388/) (2023)

## Abstract (arXiv)

This paper jointly addresses the problem of data uncertainty, popularity bias, and exposure bias in session-based recommender systems. We study the symptoms of this bias both in item embeddings and in recommendations. We propose treating user interest as a stochastic process in the latent space and providing a model-agnostic implementation of this mathematical concept. The proposed stochastic component consists of elements: debiasing item embeddings with regularization for embedding uniformity, modeling dense user interest from session prefixes, and introducing fake targets in the data to simulate extended exposure. We conducted computational experiments on two popular benchmark datasets, Diginetica and YooChoose 1/64, as well as several modifications of the YooChoose dataset with different ratios of popular items. The results show that the proposed approach allows us to mitigate the challenges mentioned.