# Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions

arXiv: https://arxiv.org/abs/2307.14906 · HTML: https://mlsanity.com/paper/2307.14906/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 27 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [On the Effectiveness of Sampled Softmax Loss for Item Recommendation](https://mlsanity.com/paper/2201.02327/) (2022)

## Built on by (most cited first, 9 of 9)

- [Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs](https://mlsanity.com/paper/2409.18721/) (2024)
- [RECE: Reduced Cross-Entropy Loss for Large-Catalogue Sequential Recommenders](https://mlsanity.com/paper/2408.02354/) (2024)
- [Autoregressive Generation Strategies for Top-K Sequential Recommendations](https://mlsanity.com/paper/2409.17730/) (2024)
- [Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems](https://mlsanity.com/paper/2407.16828/) (2024)
- [RePlay: a Recommendation Framework for Experimentation and Production Use](https://mlsanity.com/paper/2409.07272/) (2024)
- [Negative Feedback for Music Personalization](https://mlsanity.com/paper/2406.04488/) (2024)
- [Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender Systems](https://mlsanity.com/paper/2507.09566/) (2025)
- [Semantic IDs for Music Recommendation](https://mlsanity.com/paper/2507.18800/) (2025)
- [Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rank](https://mlsanity.com/paper/2507.20753/) (2025)

## Abstract (arXiv)

This work introduces TRON, a scalable session-based Transformer Recommender using Optimized Negative-sampling. Motivated by the scalability and performance limitations of prevailing models such as SASRec and GRU4Rec+, TRON integrates top-k negative sampling and listwise loss functions to enhance its recommendation accuracy. Evaluations on relevant large-scale e-commerce datasets show that TRON improves upon the recommendation quality of current methods while maintaining training speeds similar to SASRec. A live A/B test yielded an 18.14% increase in click-through rate over SASRec, highlighting the potential of TRON in practical settings. For further research, we provide access to our source code at https://github.com/otto-de/TRON and an anonymized dataset at https://github.com/otto-de/recsys-dataset.