Ranking and Pricing under a Cascade Model of Consumer Review Browsing

IF 2 3区工程技术 Q3 ENGINEERING, INDUSTRIAL

IISE Transactions Pub Date : 2023-11-02 DOI:10.1080/24725854.2023.2274898

Jingtong Zhao, Xin Pan, Van-Anh Truong, Jie Song

{"title":"Ranking and Pricing under a Cascade Model of Consumer Review Browsing","authors":"Jingtong Zhao, Xin Pan, Van-Anh Truong, Jie Song","doi":"10.1080/24725854.2023.2274898","DOIUrl":null,"url":null,"abstract":"AbstractIn online platforms, the reviews posted by consumers who arrive earlier are playing an increasingly important role in the purchasing decisions of consumers who arrive later. Motivated by this observation, we study the problems faced by a platform selling a single product with no capacity constraint, where the demand is explicitly influenced by the reviews presented to the consumers. More precisely, we model a consumer’s browsing of reviews for a single product as following a cascade click model, with each consumer seeing some initial number of reviews and forming a utility estimate for the product based on the reviews the consumer has read. In the first part of the paper, we consider how to rank the reviews to induce short- and long-term revenue-maximizing purchasing behaviors. In the second part, we study how to set the price of the product. We derive structural insights and bounds on both problems. We also consider the case that the parameters of the model are unknown, where we propose algorithms that learn the parameters and optimize the ranking of the reviews or the price online. We show that our algorithms have regrets O(T23).Keywords: Analysis of algorithmsApproximations/heuristicsRevenue managementDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IISE Transactions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/24725854.2023.2274898","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}

引用次数: 0

Abstract

AbstractIn online platforms, the reviews posted by consumers who arrive earlier are playing an increasingly important role in the purchasing decisions of consumers who arrive later. Motivated by this observation, we study the problems faced by a platform selling a single product with no capacity constraint, where the demand is explicitly influenced by the reviews presented to the consumers. More precisely, we model a consumer’s browsing of reviews for a single product as following a cascade click model, with each consumer seeing some initial number of reviews and forming a utility estimate for the product based on the reviews the consumer has read. In the first part of the paper, we consider how to rank the reviews to induce short- and long-term revenue-maximizing purchasing behaviors. In the second part, we study how to set the price of the product. We derive structural insights and bounds on both problems. We also consider the case that the parameters of the model are unknown, where we propose algorithms that learn the parameters and optimize the ranking of the reviews or the price online. We show that our algorithms have regrets O(T23).Keywords: Analysis of algorithmsApproximations/heuristicsRevenue managementDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.

查看原文本刊更多论文

消费者评论浏览级联模型下的排名与定价

在网络平台上，早到消费者发布的评论对晚到消费者的购买决策起着越来越重要的作用。在这种观察的激励下，我们研究了在没有容量限制的情况下销售单一产品的平台所面临的问题，在这种情况下，需求明显受到呈现给消费者的评论的影响。更准确地说，我们按照级联点击模型对消费者浏览单个产品的评论进行建模，每个消费者看到一些初始数量的评论，并根据消费者阅读的评论形成对产品的效用估计。在本文的第一部分中，我们考虑了如何对评论进行排序以诱导短期和长期收益最大化的购买行为。在第二部分，我们研究了如何制定产品的价格。我们在这两个问题上得到了结构性的见解和界限。我们还考虑了模型参数未知的情况，在这种情况下，我们提出了学习参数并在线优化评论或价格排名的算法。我们证明我们的算法有遗憾0 (T23)。关键词:算法分析近似/启发式收益管理免责声明作为对作者和研究人员的服务，我们提供此版本的已接受手稿(AM)。在最终出版版本记录(VoR)之前，将对该手稿进行编辑、排版和审查。在制作和印前，可能会发现可能影响内容的错误，所有适用于期刊的法律免责声明也与这些版本有关。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IISE Transactions Engineering-Industrial and Manufacturing Engineering

CiteScore

5.70

自引率

7.70%

发文量

期刊介绍： IISE Transactions is currently abstracted/indexed in the following services: CSA/ASCE Civil Engineering Abstracts; CSA-Computer & Information Systems Abstracts; CSA-Corrosion Abstracts; CSA-Electronics & Communications Abstracts; CSA-Engineered Materials Abstracts; CSA-Materials Research Database with METADEX; CSA-Mechanical & Transportation Engineering Abstracts; CSA-Solid State & Superconductivity Abstracts; INSPEC Information Services and Science Citation Index. Institute of Industrial and Systems Engineers and our publisher Taylor & Francis make every effort to ensure the accuracy of all the information (the "Content") contained in our publications. However, Institute of Industrial and Systems Engineers and our publisher Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Institute of Industrial and Systems Engineers and our publisher Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Institute of Industrial and Systems Engineers and our publisher Taylor & Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to, or arising out of the use of the Content. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions .