mlsanityData to 14 Oct 2026

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

arXiv 2504.10005 ↗First posted 14 Apr 2025cs.LG cs.AI cs.IRcited by 0

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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.

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.

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22 of 33 references.

References · 22

  1. 2020Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hyperspherecited by 1,405
  2. 2017Neural Collaborative Filteringcited by 1,111
  3. 2015Session-based Recommendations with Recurrent Neural Networkscited by 695
  4. 2018Session-based Recommendation with Graph Neural Networkscited by 312
  5. 2020Self-supervised Graph Learning for Recommendationcited by 305
  6. 2021GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networkscited by 147
  7. 2019A Survey on Session-based Recommender Systemscited by 97
  8. 2016Improved Recurrent Neural Networks for Session-based Recommendationscited by 86
  9. 2023A Survey on Popularity Bias in Recommender Systemscited by 43
  10. 2020Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systemscited by 38
  11. 2020TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendationcited by 33
  12. 2019NISER: Normalized Item and Session Representations to Handle Popularity Biascited by 12
10 more
  1. 2020Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertaintycited by 11
  2. 2022Uncertainty Quantification for Fairness in Two-Stage Recommender Systemscited by 10
  3. 2022Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendationcited by 8
  4. 2024Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systemscited by 6
  5. 2021Introducing Self-Attention to Target Attentive Graph Neural Networkscited by 4
  6. 2023Popularity Debiasing from Exposure to Interaction in Collaborative Filteringcited by 4
  7. 2023Graph-based Alignment and Uniformity for Recommendationcited by 4
  8. 2022STAR: A Session-Based Time-Aware Recommender Systemcited by 3
  9. 2023SR-PredictAO: Session-based Recommendation with High-Capability Predictor Add-Oncited by 1
  10. 2023Estimating Propensity for Causality-based Recommendation without Exposure Datacited by 1

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