The disclosure relates generally to online systems, and in particular to training a computer model to predict whether a user device is at a location based on context information received from client devices.
An online system, such as a social networking system, allows its users to connect to and communicate with other online system users. Users may create profiles on an online system that are tied to their identities and use information about the users, such as interests and demographic information. The users may be individuals or entities such as corporations or charities. Because of the increasing popularity of online systems and increasing amount of user-specific information that they maintain, an online system provides an ideal forum for content providers to increase awareness about products or services by presenting content items to online system users as stories in social networking newsfeeds or via other presentation mechanisms.
Content providers may wish to measure the effectiveness of their online system content campaigns by tracking online system users who accessed the content campaign and subsequently visited the content provider's physical location. Traditionally, online systems have relied on user “check-ins” at physical locations in order to gather user location information. However, data is collected slowly using this method and is dependent upon individual users taking an action (i.e., the check-in) at the location and designating the correct physical location. Many users do not regularly, if ever, check-in to locations. Further, there are many locations where users rarely check in (e.g., grocery stores, drug stores, dry cleaners, etc.), rendering the online system's ability to predict when a user is at those locations more difficult.