Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866915218010931200 |
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| author | Wilm, Timo Normann, Philipp Baumeister, Sophie Kobow, Paul-Vincent |
| author_facet | Wilm, Timo Normann, Philipp Baumeister, Sophie Kobow, Paul-Vincent |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_14906 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions Wilm, Timo Normann, Philipp Baumeister, Sophie Kobow, Paul-Vincent Information Retrieval Artificial Intelligence Machine Learning 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. |
| title | Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2307.14906 |