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.

翻译：本文提出TRON——一种可扩展的基于会话的Transformer推荐系统，其核心采用优化负采样策略。针对SASRec和GRU4Rec+等主流模型在可扩展性和性能上的局限性，TRON通过整合Top-K负采样与列表式损失函数来提升推荐精度。在相关大规模电商数据集上的评估表明，TRON在保持与SASRec相近训练速度的同时，显著改善了当前方法的推荐质量。在线A/B测试显示，相较于SASRec，TRON使点击率提升了18.14%，凸显了其在实际应用中的潜力。为促进后续研究，我们已在https://github.com/otto-de/TRON 提供源代码，并在https://github.com/otto-de/recsys-dataset 开放匿名化数据集。