# Towards Psychologically-Grounded Dynamic Preference Models

arXiv: https://arxiv.org/abs/2208.01534 · HTML: https://mlsanity.com/paper/2208.01534/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 63 references that are in the map.
- First posted on arXiv 1 Aug 2022.

## Builds on (most cited first, 13 of 13)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017)
- [Degenerate Feedback Loops in Recommender Systems](https://mlsanity.com/paper/1902.10730/) (2019)
- [Auditing Radicalization Pathways on YouTube](https://mlsanity.com/paper/1908.08313/) (2019)
- [Do Offline Metrics Predict Online Performance in Recommender Systems?](https://mlsanity.com/paper/2011.07931/) (2020)
- [Digital Nudging with Recommender Systems: Survey and Future Directions](https://mlsanity.com/paper/2011.03413/) (2020)
- [An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes](https://mlsanity.com/paper/2203.13769/) (2022)
- [Algorithmic Extremism: Examining YouTube's Rabbit Hole of Radicalization](https://mlsanity.com/paper/1912.11211/) (2019)
- [The closed loop between opinion formation and personalised recommendations](https://mlsanity.com/paper/1809.04644/) (2018)
- [Dynamic Poisson Factorization](https://mlsanity.com/paper/1509.04640/) (2015)
- [Recurrent Poisson Factorization for Temporal Recommendation](https://mlsanity.com/paper/1703.01442/) (2017)
- [Mechanisms and Attributes of Echo Chambers in Social Media](https://mlsanity.com/paper/2106.05401/) (2021)
- [Echo Chambers in Collaborative Filtering Based Recommendation Systems](https://mlsanity.com/paper/2011.03890/) (2020)

## Built on by (most cited first, 8 of 8)

- [Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition](https://mlsanity.com/paper/2306.14670/) (2023)
- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [Online Recommendations for Agents with Discounted Adaptive Preferences](https://mlsanity.com/paper/2302.06014/) (2023)
- [Harm Mitigation in Recommender Systems under User Preference Dynamics](https://mlsanity.com/paper/2406.09882/) (2024)
- [Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement](https://mlsanity.com/paper/2211.13585/) (2022)
- [Ads that Stick: Near-Optimal Ad Optimization through Psychological Behavior Models](https://mlsanity.com/paper/2509.20304/) (2025)
- [Delayed and Indirect Impacts of Link Recommendations](https://mlsanity.com/paper/2303.09700/) (2023)
- [Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation](https://mlsanity.com/paper/2603.03094/) (2026)

## Abstract (arXiv)

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.