Anticipating Information Needs Based on Check-in Activity

Abstract

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.

Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM '17), 2017

Cited by in corpus (5)

• Asking Clarifying Questions in Open-Domain Information-Seeking Conversations
• Reply With: Proactive Recommendation of Email Attachments
• Target Apps Selection: Towards a Unified Search Framework for Mobile Devices
• POINTREC: A Test Collection for Narrative-driven Point of Interest Recommendation
• Towards an Understanding of Entity-Oriented Search Intents