MERIT: A Merchant Incentive Ranking Model for Hotel Search & Ranking

Shigang Quan, Hailong Tan, Shui Liu,Zhenzhe Zheng,Ruihao Zhu,Liangyue Li,Quan Lu,Fan Wu

PROCEEDINGS OF THE 32ND ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2023(2023)

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摘要
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 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 service 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 service quality and performance, and conflicts between platform revenue and consumer experience. To address these challenges, we propose MERIT, a MERchant InceTive ranking model, which can simultaneously take the interests of merchants and consumers into account. We introduce information about the hotel service quality at the input-output level. At the input level, we incorporate factors of hotel service quality as features (as the underlying reasons for service quality), while at the output level, we introduce the metric Hotel Rating Score (HRS) as a label (as the evaluated outcome of service quality). 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 revenue and consumer experience. To demonstrate the effectiveness of MERIT, we compare our method with several state-of-the-art benchmarks. 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 HRS score. Based on these results, we have deployed MERIT online on Fliggy, one of the most popular OTPs in China, to serve tens of millions of consumers and hundreds of thousands of hotel merchants.
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关键词
Hotel Search & Ranking System,Monotonic Neural Networks,Hotel Service Quality
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