Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions
This work addresses scalability issues for e-commerce platforms by providing a more efficient and accurate session-based recommender system, though it is incremental as it builds on existing Transformer-based methods.
The paper tackles scalability and performance limitations in session-based recommendation systems by introducing TRON, which improves recommendation accuracy through optimized negative sampling and loss functions, achieving an 18.14% increase in click-through rate over SASRec in live A/B tests.
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.