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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.08650 |
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| _version_ | 1866912030806507520 |
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| author | Black, William Ilhan, Ercument Marchini, Andrea Markeviciute, Vilda |
| author_facet | Black, William Ilhan, Ercument Marchini, Andrea Markeviciute, Vilda |
| contents | This paper presents AdaptEx, a self-service contextual bandit platform widely used at Expedia Group, that leverages multi-armed bandit algorithms to personalize user experiences at scale. AdaptEx considers the unique context of each visitor to select the optimal variants and learns quickly from every interaction they make. It offers a powerful solution to improve user experiences while minimizing the costs and time associated with traditional testing methods. The platform unlocks the ability to iterate towards optimal product solutions quickly, even in ever-changing content and continuous "cold start" situations gracefully. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_08650 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AdaptEx: A Self-Service Contextual Bandit Platform Black, William Ilhan, Ercument Marchini, Andrea Markeviciute, Vilda Information Retrieval Artificial Intelligence Machine Learning This paper presents AdaptEx, a self-service contextual bandit platform widely used at Expedia Group, that leverages multi-armed bandit algorithms to personalize user experiences at scale. AdaptEx considers the unique context of each visitor to select the optimal variants and learns quickly from every interaction they make. It offers a powerful solution to improve user experiences while minimizing the costs and time associated with traditional testing methods. The platform unlocks the ability to iterate towards optimal product solutions quickly, even in ever-changing content and continuous "cold start" situations gracefully. |
| title | AdaptEx: A Self-Service Contextual Bandit Platform |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2308.08650 |