Value-Based Smart Reminders

Finding appropriate moments for notifications in smart reminder system

Master Thesis (2019)
Author(s)

R. Kabel (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Willem Paul Brinkman – Mentor (TU Delft - Interactive Intelligence)

M.L. Tielman – Graduation committee member (TU Delft - Interactive Intelligence)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2019 Remy Kabel
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 Remy Kabel
Graduation Date
28-03-2019
Awarding Institution
Delft University of Technology
Programme
['Electrical Engineering | Embedded Systems']
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

This project focuses on finding what defines an appropriate moment to notify in a smart reminder system. Specifically, the goal is to find a way in which smart reminders systems can be extended through the use of user values to ultimately provide more appropriately timed reminders. This is essential in providing software aided support. A system is designed from scratch, combining existing concepts of activity prediction and value-based design. A statistical Markov chain model is made from predictions based on Expectation Maximization and Apriori algorithms. User values are quantified and optimized following the concept of a Socially Adaptive Electronic Partner and added to the model to identify an appropriate moment for a notification. The concept of values is broken down into two aspects. Firstly, the value loss invoked by the nuisance of receiving a notification during a certain activity. Secondly, the expected value gain achieved by actually remembering is simulated through the expected time between the moment of notification and the deadline. The model is implemented in a Node.js web application, following the principles of a RESTful web API. The model is tested for both its success in correct prediction and the moment selection. The basic predictive model shows a 91% success rate but falls short at 73% when considering values. After optimizing the system for user values, up to a 13% improvement in the success rate and an 18% improvement in the score (more appropriate moment) is found for the model considering user values with respect to the basic, predictive model. Overall, a clear and workable approach to value-based smart reminders is shown through a statistical and dynamic approach to incorporate the concept of user values in a smart-reminder system.

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