Theses and Dissertations from DePaul University

Date of Award

Summer 2026

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College

College of Computing and Digital Media

First Advisor

Bamshad Mobasher

Abstract

Recommender systems have become deeply embedded in digital platforms such as streaming services, social media, and e-commerce applications, shaping how users discover information, consume content, and interact with online environments. Despite their widespread adoption, these systems are frequently perceived as opaque ‘black boxes’, leaving users uncertain about how recommendations are generated, why certain content is prioritized, and whether their interactions meaningfully influence recommendation outcomes. Existing research at the intersection of recommender systems and human–computer interaction has largely focused on improving transparency, explainability, and recommendation quality from a system-centered perspective. However, limited research has examined how users conceptualize recommender systems and interpret algorithmic behavior, and how these understandings shape their perceptions, interactions, and online behavior.   In this dissertation, I address this gap by investigating users’ mental models of recommender systems, their perceptions of control, and the various ways in which they attempt to exercise agency within algorithmically mediated environments. Drawing on multiple qualitative and mixed-methods studies, I examine how users construct intuitive theories of recommender systems and how these theories influence their perceptions, interactions, and behavioral strategies. To this end, I introduce the ‘Theory of the Recommender’, a framework for modeling how users conceptualize recommender systems. I further identify patterns of fragmented user understanding, perceived control, and user agency that shape how users negotiate their relationships with recommender systems. Additionally, I examine the different roles users adopt when engaging with recommender systems and how these roles influence their interactions and behavioral adaptation. Finally, I discuss the implications of these findings for the design of transparent, interpretable, and user-aligned recommender systems that better support meaningful user agency and informed interactions with these systems.

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