Context-aware Recommendations through Activity Recognition
Toon De Pessemier, Simon Dooms, Kris Vanhecke, Bart Matté, Ewout Meyns, Luc Martens · 2013
The mobile Internet introduces new opportunities to gain insight in the current user's environment, behavior, and activity.This additional contextual information can be used as an extra information source to improve traditional recommendation algorithms.This article describes a framework to detect the current context and activity of the user by analyzing data retrieved from different sensors available on mobile devices.The framework can easily be extended to detect custom activities and is built in a generic way to ensure easy integration with other applications.On top of this framework, a recommender system is built to provide a personalized content offer, consisting of relevant information such as points-of-interest, train schedules, and touristic info, based on the user's current context.Users who tested the application confirmed the usability and liked to use it.The recommendations are assessed as effective and help them to discover new places and interesting information.