MERIT: A Merchant Incentive Ranking Model for Hotel Search & Ranking
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908402026807296 |
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| author | Quan, Shigang Tan, Hailong Liu, Shui zheng, Zhenzhe Zhu, Ruihao Li, Liangyue Lu, Quan Wu, Fan |
| author_facet | Quan, Shigang Tan, Hailong Liu, Shui zheng, Zhenzhe Zhu, Ruihao Li, Liangyue Lu, Quan Wu, Fan |
| contents | Online Travel Platforms (OTPs) have been working on improving their hotel Search & Ranking (S&R) systems that facilitate efficient matching between consumers and hotels. Existing OTPs focus almost exclusively on improving platform revenue. In this work, we take a first step in incorporating hotel merchants' objectives into the design of hotel S&R systems to achieve an incentive loop: the OTP tilts impressions and better-ranked positions to merchants with high quality, and in return, the merchants provide better service to consumers. Three critical design challenges need to be resolved to achieve this incentive loop: Matthew Effect in the consumer feedback-loop, unclear relation between hotel quality and performance, and conflicts between short-term and long-term revenue. To address these challenges, we propose MERIT, a MERchant IncenTive ranking model, which can simultaneously take the interests of merchants and consumers into account. We define a new Merchant Competitiveness Index (MCI) to represent hotel merchant quality and propose a new Merchant Tower to model the relation between MCI and ranking scores. Also, we design a monotonic structure for Merchant Tower to provide a clear relation between hotel quality and performance. Finally, we propose a Multi-objective Stratified Pairwise Loss, which can mitigate the conflicts between OTP's short-term and long-term revenue. The offline experiment results indicate that MERIT outperforms these methods in optimizing the demands of consumers and merchants. Furthermore, we conduct an online A/B test and obtain an improvement of 3.02% for the MCI score. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08442 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MERIT: A Merchant Incentive Ranking Model for Hotel Search & Ranking Quan, Shigang Tan, Hailong Liu, Shui zheng, Zhenzhe Zhu, Ruihao Li, Liangyue Lu, Quan Wu, Fan Information Retrieval Online Travel Platforms (OTPs) have been working on improving their hotel Search & Ranking (S&R) systems that facilitate efficient matching between consumers and hotels. Existing OTPs focus almost exclusively on improving platform revenue. In this work, we take a first step in incorporating hotel merchants' objectives into the design of hotel S&R systems to achieve an incentive loop: the OTP tilts impressions and better-ranked positions to merchants with high quality, and in return, the merchants provide better service to consumers. Three critical design challenges need to be resolved to achieve this incentive loop: Matthew Effect in the consumer feedback-loop, unclear relation between hotel quality and performance, and conflicts between short-term and long-term revenue. To address these challenges, we propose MERIT, a MERchant IncenTive ranking model, which can simultaneously take the interests of merchants and consumers into account. We define a new Merchant Competitiveness Index (MCI) to represent hotel merchant quality and propose a new Merchant Tower to model the relation between MCI and ranking scores. Also, we design a monotonic structure for Merchant Tower to provide a clear relation between hotel quality and performance. Finally, we propose a Multi-objective Stratified Pairwise Loss, which can mitigate the conflicts between OTP's short-term and long-term revenue. The offline experiment results indicate that MERIT outperforms these methods in optimizing the demands of consumers and merchants. Furthermore, we conduct an online A/B test and obtain an improvement of 3.02% for the MCI score. |
| title | MERIT: A Merchant Incentive Ranking Model for Hotel Search & Ranking |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2506.08442 |