Towards Psychologically-Grounded Dynamic Preference Models

TL;DR

This paper introduces a methodology for developing psychologically grounded dynamic preference models in recommendation systems, demonstrating how classic psychological effects can inform more realistic and effective user modeling.

Contribution

The paper proposes a general methodology for creating psychologically plausible dynamic preference models and demonstrates its application with models based on classic psychological effects.

Findings

Psychological models show distinct behaviors relevant for system design

The methodology critiques existing models for limited psychological grounding

Implications for evaluation metrics like engagement and diversity

Abstract

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.