Scaling Session-Based Transformer Recommendations using Optimized
  Negative Sampling and Loss Functions
Timo Wilm Philipp Normann Sophie Baumeister Paul-Vincent Kobow
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.