Deep Learning-Based Imputation Method to Enhance Crowdsourced Data on Online Business Directory Platforms for Improved Services

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.

KEYWORDS:

• Crowdsourced data
• missing value imputation
• deep learning
• online business directory

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/07421222.2023.2196770

Notes

¹ http://www.statista.com/statistics/315709/online-local-business-search-frequency-us-canada/#statisticContainer, accessed on April 20, 2022.

² https://www.yelp-ir.com, accessed on April 20, 2022.

³ On Yelp, each restaurant is described by more than 80 attributes, for which most values are crowdsourced.

⁴ https://blog.yelp.com/2019/10/study-shows-97-of-people-buy-from-local-businesses-they-discover-on-yelp, accessed on April 20, 2022.

⁵ https://support.google.com/business/answer/9049526?hl=en, accessed on April 20, 2022.

⁶ The imputation process is performed iteratively until it reaches a prespecified termination threshold.

⁷ https://www.yelp.com/dataset.

Additional information

Notes on contributors

Da Xu

Da Xu is Assistant Professor of Information Systems at the College of Business, California State University Long Beach. He received his PhD in Business Administration from the University of Utah. Dr. Xu’s research interests include predictive analytics, health informatics, online digital platforms, deep learning, and data mining for business intelligence. He has published in Journal of the American Medical Informatics Association, IEEE Journal of Biomedical and Health Informatics, and Journal of Biomedical Informatics. Email: [email protected]

Paul Jen-Hwa Hu

Paul Jen-Hwa Hu is ER Dumke Jr Presidential Endowed Chair in Business at the David Eccles School of Business, University of Utah. He received his PhD in Management Information Systems from University of Arizona. His research interests include information technology for health care, technology implementation and management, business analytics, digital transformation, and technology-enabled learning and knowledge management. Dr. Hu has published in Journal of Management Information Systems, MIS Quarterly, Information Systems Research, Journal of the AIS, European Journal of Information Systems, Decision Sciences, Journal of Medical Internet Research, Journal of the American Medical Informatics Association, other journals, and various IEEE and ACM transaction journals. Email: [email protected]

Xiao Fang

Xiao Fang is Professor of MIS and JPMorgan Chase Senior Fellow at Lerner College of Business & Economics and Institute for Financial Services Analytics at University of Delaware, where he also holds appointments at Departments of Computer Science and Electrical and Computer Engineering. Dr. Fang’s research focuses on financial technology, social network analytics, and health care analytics, with methods and tools drawn from reference disciplines including Computer Science (e.g., machine learning) and Management Science (e.g., optimization). He has published in business journals including Journal of Management Information Systems, Management Science, Operations Research, MIS Quarterly, and Information Systems Research as well as computer science outlets such as ACM Transactions on Information Systems and IEEE Transactions on Knowledge and Data Engineering. Dr. Fang received an INFORMS Design Science Award and co-founded INFORMS Workshop on Data Science. He serves as an Associate Editor for INFORMS Journal on Data Science and Service Science. Email: [email protected]