# Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems

arXiv: https://arxiv.org/abs/2305.17428 · HTML: https://mlsanity.com/paper/2305.17428/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 27 May 2023.

## Builds on (most cited first, 15 of 15)

- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [Why Is My Classifier Discriminatory?](https://mlsanity.com/paper/1805.12002/) (2018)
- [How Do Classifiers Induce Agents To Invest Effort Strategically?](https://mlsanity.com/paper/1807.05307/) (2018)
- [The Role of Randomness and Noise in Strategic Classification](https://mlsanity.com/paper/2005.08377/) (2020)
- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers](https://mlsanity.com/paper/1806.00955/) (2018)
- [Supply-Side Equilibria in Recommender Systems](https://mlsanity.com/paper/2206.13489/) (2022)
- [The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization](https://mlsanity.com/paper/2202.11776/) (2022)
- [From Optimizing Engagement to Measuring Value](https://mlsanity.com/paper/2008.12623/) (2020)
- [Strategic Ranking](https://mlsanity.com/paper/2109.08240/) (2021)
- [A/B Testing for Recommender Systems in a Two-sided Marketplace](https://mlsanity.com/paper/2106.00762/) (2021)
- [Personalized Reward Learning with Interaction-Grounded Learning (IGL)](https://mlsanity.com/paper/2211.15823/) (2022)
- ["So, Tell Me What Users Want, What They Really, Really Want!"](https://mlsanity.com/paper/1803.02065/) (2018)
- [Ratioing the President: An exploration of public engagement with Obama and Trump on Twitter](https://mlsanity.com/paper/2006.03526/) (2020)

## Built on by (most cited first, 7 of 7)

- [Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms](https://mlsanity.com/paper/2401.09804/) (2024)
- [Multi-Objective Recommendation via Multivariate Policy Learning](https://mlsanity.com/paper/2405.02141/) (2024)
- [Value Alignment of Social Media Ranking Algorithms](https://mlsanity.com/paper/2509.14434/) (2025)
- [CTRL-Rec: Controlling Recommender Systems With Natural Language](https://mlsanity.com/paper/2510.12742/) (2025)
- [System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes](https://mlsanity.com/paper/2406.01611/) (2024)
- [MultiScale Contextual Bandits for Long Term Objectives](https://mlsanity.com/paper/2503.17674/) (2025)
- [A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems](https://mlsanity.com/paper/2605.16344/) (2026)

## Abstract (arXiv)

Many recommender systems optimize a linear weighting of different user behaviors, such as clicks, likes, and shares. We analyze the optimal choice of weights from the perspectives of both users and content producers who strategically respond to the weights. We consider three aspects of each potential behavior: value-faithfulness (how well a behavior indicates whether the user values the content), strategy-robustness (how hard it is for producers to manipulate the behavior), and noisiness (how much estimation error there is in predicting the behavior). Our theoretical results show that for users, up-weighting more value-faithful and less noisy behaviors leads to higher utility, while for producers, up-weighting more value-faithful and strategy-robust behaviors leads to higher welfare (and the impact of noise is non-monotonic). Finally, we apply our framework to design weights on Facebook, using a large-scale dataset of approximately 70 million URLs shared on Facebook. Strikingly, we find that our user-optimal weight vector (a) delivers higher user value than a vector not accounting for variance; (b) also enhances broader societal outcomes, reducing misinformation and raising the quality of the URL domains, outcomes that were not directly targeted in our theoretical framework.