  Designing recommendation systems that serve content aligned with time varying
preferences requires proper accounting of the feedback effects of
recommendations on human behavior and psychological condition. We argue that
modeling the influence of recommendations on people's preferences must be
grounded in psychologically plausible models. We contribute a methodology for
developing grounded dynamic preference models. We demonstrate this method with
models that capture three classic effects from the psychology literature:
Mere-Exposure, Operant Conditioning, and Hedonic Adaptation. We conduct
simulation-based studies to show that the psychological models manifest
distinct behaviors that can inform system design. Our study has two direct
implications for dynamic user modeling in recommendation systems. First, the
methodology we outline is broadly applicable for psychologically grounding
dynamic preference models. It allows us to critique recent contributions based
on their limited discussion of psychological foundation and their implausible
predictions. Second, we discuss implications of dynamic preference models for
recommendation systems evaluation and design. In an example, we show that
engagement and diversity metrics may be unable to capture desirable
recommendation system performance.
