arXiv:2506.08442cs.IR2025-06被引 1

让酒店质量好者获得更多曝光,形成平台与商家双赢的推荐机制。

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

  • 引入商户竞争力指数MCI,量化酒店质量并指导排序
  • 通过分层配对损失实现短期收益与长期激励的平衡
  • 在线实验提升商户竞争力3.02%,兼顾用户与商家利益

在线旅游平台(OTPs)致力于优化酒店搜索与排名系统,以高效匹配消费者与酒店。现有系统几乎仅关注平台收益,本文首次将酒店商户目标纳入排名设计,构建良性激励循环:平台将更高曝光和排名给予高质量商户,商户则提升服务质量。为实现该循环,需解决三大挑战:消费者反馈中的马太效应、酒店质量与表现关系模糊、短期与长期收益冲突。为此提出MERIT模型,定义新的商户竞争力指数(MCI)表征酒店质量,设计商户塔(Merchant Tower)建模MCI与排名分数的关系,并采用单调结构确保质量与表现的清晰关联。同时提出多目标分层配对损失,缓解短期与长期收益矛盾。离线实验表明MERIT在满足消费者与商户需求方面优于基线方法;在线A/B测试中MCI得分提升3.02%。

原文摘要 · Abstract (English)

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.

推荐系统商户激励酒店搜索多目标优化

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