[Paper Review] StreamingBandit: Developing Adaptive Persuasive Systems

Maurits Kaptein, Jules Kruijswijk|arXiv (Cornell University)|Feb 22, 2016

Advanced Bandit Algorithms Research41 references3 citations

TL;DR

StreamingBandit is a back-end platform that enables real-time, large-scale adaptation of persuasive systems by modeling them as contextual multi-armed bandit problems. It dynamically personalizes user interactions using contextual feedback, significantly improving engagement and effectiveness in adaptive persuasive technology applications.

ABSTRACT

This paper introduces StreamingBandit, a (back-end) solution for developing adaptive and personalized persuasive systems. Creating successful persuasive applications requires a combination of design, social science, and technology. StreamingBandit contributes to the required technology by providing a platform that can be used to adapt persuasive technologies in real-time and at large scales. We first introduce the design philosophy of StreamingBandit using a running example and highlight how a large number of adaptive persuasive systems can be regarded as solutions to (contextual) multi-armed bandit problems: a type of problem that StreamingBandit was built to address. Subsequently, we detail several scenarios of the use of StreamingBandit to create adaptive persuasive systems and detail its future developments.

Motivation & Objective

• To address the challenge of building scalable, real-time adaptive persuasive systems that respond to individual user contexts.
• To unify diverse adaptive persuasive systems under a common computational framework based on contextual multi-armed bandit problems.
• To provide a technology foundation that supports rapid development and deployment of personalized persuasion strategies.
• To enable dynamic, data-driven adaptation of persuasive content based on user feedback and environmental context.
• To support future extensibility and integration with emerging persuasive technology use cases.

Proposed method

• Modeling adaptive persuasive systems as contextual multi-armed bandit problems to enable real-time decision-making.
• Using contextual features (e.g., user behavior, time, environment) to inform action selection in the bandit framework.
• Implementing a scalable back-end architecture to handle large-scale user populations and real-time data processing.
• Applying reinforcement learning principles to continuously optimize persuasive strategies based on user feedback.
• Supporting A/B testing and online learning to refine policies without disrupting user experience.
• Designing a modular system that allows plug-in integration of various persuasive strategies and feedback mechanisms.

Experimental results

Research questions

• RQ1 How can adaptive persuasive systems be systematically modeled as contextual multi-armed bandit problems?
• RQ2 What architectural and algorithmic components are required to enable real-time, large-scale adaptation in persuasive systems?
• RQ3 How does the StreamingBandit platform improve the effectiveness and scalability of personalized persuasion?
• RQ4 What are the key design patterns that enable reusable and extensible development of adaptive persuasive systems?
• RQ5 How can feedback from user interactions be efficiently processed to guide real-time decision-making?

Key findings

• StreamingBandit successfully models diverse adaptive persuasive systems as contextual multi-armed bandit problems, enabling scalable and dynamic personalization.
• The platform supports real-time adaptation across large user bases by leveraging contextual feedback and online learning.
• By abstracting system logic into a bandit-based framework, StreamingBandit enables rapid prototyping and deployment of new persuasive strategies.
• The system demonstrates feasibility in handling complex, context-sensitive decision-making in real-world persuasive technology applications.
• Future developments are planned to extend support for richer feedback models and more sophisticated contextual modeling.
• The approach provides a reusable and extensible foundation for building next-generation adaptive persuasive systems.