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Forecasting Venue Popularity on Location‐based Services Using Interpretable Machine Learning by Lei Wang; Ram Gopal; Ramesh Shankar; Joseph Pancras is a Business, Management and Accounting article available to read on EtoBox.

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## Abstract Customers are increasingly utilizing location‐based services via mobile devices to engage with retail establishments. The focus of this paper is to identify factors that help to drive venue popularity revealed by location‐based services, which then better facilitate companies’ operational decisions, such as procurement and staff scheduling. Using data collected from Foursquare and Yelp, we build, evaluate, and compare a wide variety of machine learning methods including deep learning models with varying characteristics and degrees of sophistication. First, we find that support vector regression is the best performing model compared to other complex predictive algorithms. Second, we apply SHAP (Shapley Additive exPlanations) to quantify the contribution from each business feature at both the global and local levels. The global interpretability results show that customer loyalty, the agglomeration effect, and the word‐of‐mouth effect are the top three drivers of venue popularity. Furthermore, the local interpretability analysis reveals that the contributions of business features vary, both quantitatively and directionally. Our findings are robust with respect to different

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Author

Lei Wang; Ram Gopal; Ramesh Shankar; Joseph Pancras

Publisher

Wiley

Published

2022

Language

EN

Field

Business, Management and Accounting (Social Sciences)