A Latent Customer Flow Model for Interpretable Predictions of Check-In Counts

Location-based services, such as Foursquare, Google Places, and Yelp, offer rich data on places that users visit, i. e., check-ins from points-of-interest (POIs). By mining check-in data, one can make predictions regarding the expected number of check-ins that new POIs will generate. Such predictions are highly relevant for location planning at both companies and public bodies (i. e., to estimate the expected customer visits of retail sites or public facilities). In this work, we propose a new machine learning approach for modeling check-in counts with the distinctive property of being highly interpretable. Specifically, our objective is to model the latent customer flow between retail site locations. To this end, we model the expected check-in count such that it is lowered due to competition among nearby POIs, while additional check-ins can be attracted when customers transition among complementary POIs. Our latent customer flow model is then extended by additional sources of spatial heterogeneity. The model is demonstrated based on data from 2.7 million user check-ins from retail stores across three different cities. As a result, our model yields accurate predictions of check-in counts while simultaneously achieving a high degree of interpretability. The latter is achieved by deriving a tailored multi-variate fixed-point iteration.