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

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

URL: http://arxiv.org/abs/2307.14906v1
Date: Thu, 27 Jul 2023 14:47:38 GMT
Title: Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions
Authors: Timo Wilm, Philipp Normann, Sophie Baumeister, Paul-Vincent Kobow
Abstract summary: TRON is a session-based Transformer Recommender using optimized negative-sampling. 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.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
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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