Anticipating Information Needs Based on Check-in Activity

TL;DR

This paper presents a method for a smart personal assistant to predict users' information needs based on their check-in activities, enhancing mobile dashboard relevance through activity inference and temporal modeling.

Contribution

It introduces a novel approach translating check-in activities into information needs and selecting relevant information cards considering future activity scenarios.

Findings

The approach effectively predicts relevant information cards based on check-in activity.

Incorporating temporal dynamics improves prediction accuracy.

Experimental results validate the method's effectiveness using real data.

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.