All Theses and Dissertations
Degree
Doctor of Philosophy in Computer Science
Faculty / School
School of Mathematics and Computer Science (SMCS)
Department
Department of Computer Science
Date of Award
Fall 2024
Advisor
Dr. Zaheeruddin Asif, Assistant Professor School of Mathematics and Computer Science (SMCS), Institute of Business Administration (IBA), Karachi
Committee Member 1
Dr. Imran Ahmed Siddiqui, Examiner, University of Karachi, Karachi
Committee Member 2
Dr. Suleman Shahid, Examiner, LUMS, Lahore
Project Type
Dissertation
Access Type
Restricted Access
Document Version
Final
Pages
119
Keywords
Decision Support Systems, Human Decision Making, Information Systems, NeuroIS, Recommender Systems, EEG.
Subjects
Artificial Intelligence, Cognitive Psychology, Computer Science, Data processing, Databases and Information Systems, Management Information Systems, Other Psychology, Psychology, Quantitative Analysis, Special computer methods
Abstract
Human decision-making behavior is an intelligent behavior which is worth replicating to enhance the capacity of intelligent systems for providing user assistance in decision making. Such a replication would reduce the effort and task complexity on behalf of the user, improve the overall user experience, and affect the degree of intelligence exhibited by the system. This paper explores individuals’ decision-making processes when using recommender systems, and its related outcomes. Based on neurofeedback of healthy human subjects, this study highlights the constructs mapped by brain regions associated with choice decision-making in recommender systems. In this study, human decision making (HDM) refers to the selection of an item from a given set of options that are shown as recommendations to a user. A recommender system is an online platform that suggests items to users based on multiple criteria. The goal of our study was to identify IS constructs that contribute towards such decision-making, thereby contributing towards creating a mental model of HDM. This was achieved through recording Electroencephalographic (EEG) readings of subjects while they performed a decision-making activity. Readings from 16 righthanded healthy avid readers reflect that reward, theory of mind, risk, calculation, task intention and emotion are the primary constructs that users employ while making a decision from a given set of recommendations in an online bookstore. The identified constructs would help in developing a design theory for enhancing user assistance, especially in the context of recommender systems.
Link to Catalog Record
https://ils.iba.edu.pk/cgi-bin/koha/opac-detail.pl?biblionumber=121252
Recommended Citation
Quazilbash, N. (2024). Human Decision Making in Recommender Systems (Unpublished doctoral dissertation). Institute of Business Administration, Pakistan. Retrieved from https://ir.iba.edu.pk/etd/89
