Inter-Category Variation in Location Search

Abstract

When searching for place entities such as businesses or points of interest, the desired place may be close (finding the nearest ATM) or far away (finding a hotel in another city). Understanding the role of distance in predicting user interests can guide the design of location search and recommendation systems. We analyze a large dataset of location searches on GPS-enabled mobile devices with 15 location categories. We model user-location distance based on raw geographic distance (kilometers) and intervening opportunities (nth closest). Both models are helpful in predicting user interests, with the intervening opportunity model performing somewhat better. We find significant inter-category variation. For instance, the closest movie theater is selected in 17.7% of cases, while the closest restaurant in only 2.1% of cases. Overall, we recommend taking category information into account when modeling location preferences of users in search and recommendation systems.