Deep Domain Specialisation for single-model multi-domain learning to rank
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916307887194112 |
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| author | Missault, Paul Felfel, Abdelmaseeh |
| author_facet | Missault, Paul Felfel, Abdelmaseeh |
| contents | Information Retrieval (IR) practitioners often train separate ranking models for different domains (geographic regions, languages, stores, websites,...) as it is believed that exclusively training on in-domain data yields the best performance when sufficient data is available. Despite their performance gains, training multiple models comes at a higher cost to train, maintain and update compared to having only a single model responsible for all domains. Our work explores consolidated ranking models that serve multiple domains. Specifically, we propose a novel architecture of Deep Domain Specialisation (DDS) to consolidate multiple domains into a single model. We compare our proposal against Deep Domain Adaptation (DDA) and a set of baseline for multi-domain models. In our experiments, DDS performed the best overall while requiring fewer parameters per domain as other baselines. We show the efficacy of our method both with offline experimentation and on a large-scale online experiment on Amazon customer traffic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01069 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Domain Specialisation for single-model multi-domain learning to rank Missault, Paul Felfel, Abdelmaseeh Information Retrieval Information Retrieval (IR) practitioners often train separate ranking models for different domains (geographic regions, languages, stores, websites,...) as it is believed that exclusively training on in-domain data yields the best performance when sufficient data is available. Despite their performance gains, training multiple models comes at a higher cost to train, maintain and update compared to having only a single model responsible for all domains. Our work explores consolidated ranking models that serve multiple domains. Specifically, we propose a novel architecture of Deep Domain Specialisation (DDS) to consolidate multiple domains into a single model. We compare our proposal against Deep Domain Adaptation (DDA) and a set of baseline for multi-domain models. In our experiments, DDS performed the best overall while requiring fewer parameters per domain as other baselines. We show the efficacy of our method both with offline experimentation and on a large-scale online experiment on Amazon customer traffic. |
| title | Deep Domain Specialisation for single-model multi-domain learning to rank |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2407.01069 |