Anticipating Information Needs Based on Check-in Activity
This work addresses the problem of personalizing mobile dashboards for users of location-based services, though it is incremental as it builds on existing context-aware systems.
The paper tackles the challenge of anticipating a user's information needs from check-in activity on location-based social networks, proposing a method that selects top information cards to maximize satisfaction across future scenarios and demonstrating its effectiveness experimentally.
In this work we address the development of a smart personal assistant that is capable of anticipating a user's information needs based on a novel type of context: the person's activity inferred from her check-in records on a location-based social network. Our main contribution is a method that translates a check-in activity into an information need, which is in turn addressed with an appropriate information card. This task is challenging because of the large number of possible activities and related information needs, which need to be addressed in a mobile dashboard that is limited in size. Our approach considers each possible activity that might follow after the last (and already finished) activity, and selects the top information cards such that they maximize the likelihood of satisfying the user's information needs for all possible future scenarios. The proposed models also incorporate knowledge about the temporal dynamics of information needs. Using a combination of historical check-in data and manual assessments collected via crowdsourcing, we show experimentally the effectiveness of our approach.