A Comparative Study on Reward Models for UI Adaptation with Reinforcement Learning
CoRR(2023)
摘要
Adapting the User Interface (UI) of software systems to user requirements and
the context of use is challenging. The main difficulty consists of suggesting
the right adaptation at the right time in the right place in order to make it
valuable for end-users. We believe that recent progress in Machine Learning
techniques provides useful ways in which to support adaptation more
effectively. In particular, Reinforcement learning (RL) can be used to
personalise interfaces for each context of use in order to improve the user
experience (UX). However, determining the reward of each adaptation alternative
is a challenge in RL for UI adaptation. Recent research has explored the use of
reward models to address this challenge, but there is currently no empirical
evidence on this type of model. In this paper, we propose a confirmatory study
design that aims to investigate the effectiveness of two different approaches
for the generation of reward models in the context of UI adaptation using RL:
(1) by employing a reward model derived exclusively from predictive
Human-Computer Interaction (HCI) models (HCI), and (2) by employing predictive
HCI models augmented by Human Feedback (HCI HF). The controlled experiment will
use an AB/BA crossover design with two treatments: HCI and HCI HF. We shall
determine how the manipulation of these two treatments will affect the UX when
interacting with adaptive user interfaces (AUI). The UX will be measured in
terms of user engagement and user satisfaction, which will be operationalized
by means of predictive HCI models and the Questionnaire for User Interaction
Satisfaction (QUIS), respectively. By comparing the performance of two reward
models in terms of their ability to adapt to user preferences with the purpose
of improving the UX, our study contributes to the understanding of how reward
modelling can facilitate UI adaptation using RL.
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关键词
ui adaptation,reward models,reinforcement learning
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