How does the User's Knowledge of the Recommender Influence their Behavior?

TL;DR

This paper investigates how users' understanding of recommender systems influences their behavior, revealing that users develop sophisticated mental models and adapt their interactions accordingly, which can impact recommendation effectiveness.

Contribution

It provides empirical evidence on users' mental models of recommenders and how this knowledge affects their interaction strategies and system performance.

Findings

Users possess an intuitive understanding of recommender systems.

Users modify their behavior based on their knowledge and intentions.

User behavior can influence recommendation quality.

Abstract

Recommender systems have become a ubiquitous part of modern web applications. They help users discover new and relevant items. Today's users, through years of interaction with these systems have developed an inherent understanding of how recommender systems function, what their objectives are, and how the user might manipulate them. We describe this understanding as the Theory of the Recommender. In this study, we conducted semi-structured interviews with forty recommender system users to empirically explore the relevant factors influencing user behavior. Our findings, based on a rigorous thematic analysis of the collected data, suggest that users possess an intuitive and sophisticated understanding of the recommender system's behavior. We also found that users, based upon their understanding, attitude, and intentions change their interactions to evoke desired recommender behavior. Finally, we discuss the potential implications of such user behavior on recommendation performance.