Inferring the Everyday Task Capabilities of Locations

Abstract

People rapidly learn the capabilities of a new location, without observing every service and product. Instead they map a few observations to familiar clusters of capabilities. This paper proposes a similar approach to computer discovery of routine location capabilities, applying machine learning to predict unobserved capabilities based on a combination of a small body of local observations and a larger body of data that is not specific to the location. We propose using the time and place of deleting items from a to-do list application to provide the local data. For reminder purposes, an area within easy walking distance is a single location, but may contain many different shops and services, collectively offering its own combination of capabilities. Truncated singular value decomposition maps the observations to combinations of features, rather than to a single cluster. Simulations, using distributions derived from real world data, demonstrate the feasibility of this approach.