ABSTRACT

Popular online business directory (OBD) platforms, such as Yelp and TripAdvisor, depend on voluntarily user-submitted data about various businesses to assist consumers in finding appropriate options for transactions. Yet the crowdsourced nature of such data restricts the availability of attribute values for many businesses on the platform. Crowdsourced data often suffer serious completeness and timeliness constraints, with negative implications for key stakeholders such as users, businesses, and the platform. We thus develop a novel, deep learning–based imputation method, premised in institutional theory, to estimate missing attribute values of individual businesses on an OBD platform. The proposed method leverages a deep model architecture and considers both inter-business and inter-attribute relationships for imputations. An application to a Yelp data set reveals our method’s greater imputation effectiveness relative to prevalent methods. To illustrate the method’s practical utilities and values, we further examine the efficacy of business recommendations empowered by its imputed business attribute values, in comparison with those enabled by data imputed by benchmark methods. The results affirm that the proposed method substantially outperforms benchmarks for imputing missing attribute values and empowers more effective business recommendations. This study addresses crucial, prominent completeness and timeliness constraints in crowdsourced data on OBD platforms and offers insights for downstream applications that can improve user experiences, firm performance, and platform services.

摘要

Yelp 和 TripAdvisor 等流行的在线企业名录 (OBD) 平台依靠用户自愿提交的有关各种企业的数据来帮助消费者找到合适的交易选项。然而，此类数据的众包性质限制了平台上许多企业属性值的可用性。众包数据通常会受到严重的完整性和及时性限制，对用户、企业和平台等关键利益相关者产生负面影响。因此，我们开发了一种新颖的、基于深度学习的插补方法，以制度理论为前提，来估计 OBD 平台上个体企业缺失的属性值。所提出的方法利用深层模型架构，并考虑插补的业务间和属性间关系。Yelp 数据集的应用揭示了我们的方法相对于流行方法具有更高的插补有效性。为了说明该方法的实际效用和价值，我们进一步检查了由其估算的业务属性值所支持的业务建议的有效性，并将其与由基准方法估算的数据所支持的建议进行了比较。结果证实，所提出的方法大大优于估算缺失属性值的基准，并能够提供更有效的业务建议。这项研究解决了 OBD 平台上众包数据中关键的、突出的完整性和及时性限制，并为下游应用程序提供了见解，以改善用户体验、公司绩效和平台服务。