Towards Scrutable Decision Tree-based User Model utilising Interactive and Interpretable Machine Learning (SUM-IML)
Citation:
Mahmoud, Dima Saber, Towards Scrutable Decision Tree-based User Model utilising Interactive and Interpretable Machine Learning (SUM-IML), Trinity College Dublin.School of Computer Science & Statistics, 2021Download Item:
Dissertation_DimaMahmoud_FinalSubmission.pdf (Accepted for publication (author's copy) - Peer Reviewed) 1.353Mb
Abstract:
Personalising user models has gained considerable attention in
recent literature. In an information-rich environment, it is crucial not
only to provide the information at any time, at any place, and in any
form, but to also minimise information overload for the user and ease
their ability to access relevant information in their user model.
Supplying tailored information and providing offerings that suit each
user's interests may be enhanced by involving the user.
Recently, much research has been concerned with employing
Machine Learning for user modelling. Machine Learning (ML) tries to
mimic and predict a user s activities, however, it cannot model the
user themselves. Users can benefit from involvement in the modelling
process by incorporating their input as considerations in modelling
through employing interactive Machine Learning or human-in-theloop controls. Such user involvement needs the user to understand
the model behaviour to ensure their inputs are effective. This can be
achieved by utilising Machine Learning interpretability techniques.
This work proposes an interpretation of the model to the user in order
to provide the user with better understanding for the model
behaviour.
In this study, the proposed approach, termed SUM-IML (Scrutable
User Modelling using Interactive and Interpretable Machine
Learning)., implements model scrutability by combining the benefits
of interactive ML as well as Interpretable ML in user modelling. This
thesis presents the research question driving this work, a state-ofthe-art review of user modelling, scrutability, interactive,
interpretable Machine Learning literature and the evaluation
methodologies required. It then presents two related experiments
that demonstrate the exploration of research question through their
results and the conclusion.
Author's Homepage:
https://tcdlocalportal.tcd.ie/pls/EnterApex/f?p=800:71:0::::P71_USERNAME:MAHMOUDDDescription:
APPROVED
Author: Mahmoud, Dima Saber
Advisor:
Conlan, OwenPublisher:
Trinity College Dublin. School of Computer Science & Statistics. Discipline of Computer ScienceType of material:
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