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

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 atthis https URLand an anonymized dataset atthis https URL.

@article{wilm2025_2307.14906,
  title={ Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions },
  author={ Timo Wilm and Philipp Normann and Sophie Baumeister and Paul-Vincent Kobow },
  journal={arXiv preprint arXiv:2307.14906},
  year={ 2025 }
}