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

Autor:	Wilm, Timo, Normann, Philipp, Baumeister, Sophie, Kobow, Paul-Vincent
Rok vydání:	2023
Předmět:	Computer Science - Information Retrieval Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1145/3604915.3610236
Popis:	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. Comment: Accepted at the Seventeenth ACM Conference on Recommender Systems (RecSys '23)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.14906 Zobrazit plný text záznamu View this record from Arxiv