  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.
