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arXiv · 2307.14906

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

Timo Wilm·Philipp Normann·Sophie Baumeister·Paul-Vincent Kobow
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Abstract

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.

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Timo Wilm, Philipp Normann, Sophie Baumeister, Paul-Vincent Kobow. 2023-07-27. Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions. https://doi.org/10.1145/3604915.3610236

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