# Human Interaction with Recommendation Systems

arXiv: https://arxiv.org/abs/1703.00535 · HTML: https://mlsanity.com/paper/1703.00535/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2017.

## Built on by (most cited first, 15 of 15)

- [Data Feedback Loops: Model-driven Amplification of Dataset Biases](https://mlsanity.com/paper/2209.03942/) (2022)
- [Preference Dynamics Under Personalized Recommendations](https://mlsanity.com/paper/2205.13026/) (2022)
- [Breaking Feedback Loops in Recommender Systems with Causal Inference](https://mlsanity.com/paper/2207.01616/) (2022)
- [T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems](https://mlsanity.com/paper/2107.08959/) (2021)
- [Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions](https://mlsanity.com/paper/2405.08027/) (2024)
- [Correcting the User Feedback-Loop Bias for Recommendation Systems](https://mlsanity.com/paper/2109.06037/) (2021)
- [In the Eye of the Beholder: Robust Prediction with Causal User Modeling](https://mlsanity.com/paper/2206.00416/) (2022)
- [Incentive-Aware Recommender Systems in Two-Sided Markets](https://mlsanity.com/paper/2211.15381/) (2022)
- [Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement](https://mlsanity.com/paper/2211.13585/) (2022)
- [Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces](https://mlsanity.com/paper/2306.10606/) (2023)
- [Synthetic Data and Simulators for Recommendation Systems: Current State and Future Directions](https://mlsanity.com/paper/2112.11022/) (2021)
- [Counterfactual Augmentation for Multimodal Learning Under Presentation Bias](https://mlsanity.com/paper/2305.14083/) (2023)
- [Bandit based centralized matching in two-sided markets for peer to peer lending](https://mlsanity.com/paper/2105.02589/) (2021)
- [One-shot Machine Teaching: Cost Very Few Examples to Converge Faster](https://mlsanity.com/paper/2212.06416/) (2022)
- [DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops](https://mlsanity.com/paper/2311.05864/) (2023)

## Abstract (arXiv)

Many recommendation algorithms rely on user data to generate recommendations. However, these recommendations also affect the data obtained from future users. This work aims to understand the effects of this dynamic interaction. We propose a simple model where users with heterogeneous preferences arrive over time. Based on this model, we prove that naive estimators, i.e. those which ignore this feedback loop, are not consistent. We show that consistent estimators are efficient in the presence of myopic agents. Our results are validated using extensive simulations.