# The Bandwagon Effect: Not Just Another Bias

arXiv: https://arxiv.org/abs/2206.12701 · HTML: https://mlsanity.com/paper/2206.12701/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 71 references that are in the map.
- First posted on arXiv 25 Jun 2022.

## Builds on (most cited first, 10 of 10)

- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://mlsanity.com/paper/1602.05352/) (2016)
- [Unbiased Learning-to-Rank with Biased Feedback](https://mlsanity.com/paper/1608.04468/) (2016)
- [Causal Intervention for Leveraging Popularity Bias in Recommendation](https://mlsanity.com/paper/2105.06067/) (2021)
- [Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System](https://mlsanity.com/paper/2010.15363/) (2020)
- [Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback](https://mlsanity.com/paper/1909.03601/) (2019)
- [Counterfactual Risk Minimization: Learning from Logged Bandit Feedback](https://mlsanity.com/paper/1502.02362/) (2015)
- [Unbiased Learning for the Causal Effect of Recommendation](https://mlsanity.com/paper/2008.04563/) (2020)
- [Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank](https://mlsanity.com/paper/2005.11938/) (2020)
- [Learning from User Interactions with Rankings: A Unification of the Field](https://mlsanity.com/paper/2012.06576/) (2020)
- [Causality-Aware Neighborhood Methods for Recommender Systems](https://mlsanity.com/paper/2012.09442/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions](https://mlsanity.com/paper/2308.03186/) (2023)
- [Anomaly Resilient Temporal QoS Prediction using Hypergraph Convoluted Transformer Network](https://mlsanity.com/paper/2410.17762/) (2024)

## Abstract (arXiv)

Optimizing recommender systems based on user interaction data is mainly seen as a problem of dealing with selection bias, where most existing work assumes that interactions from different users are independent. However, it has been shown that in reality user feedback is often influenced by earlier interactions of other users, e.g. via average ratings, number of views or sales per item, etc. This phenomenon is known as the bandwagon effect. In contrast with previous literature, we argue that the bandwagon effect should not be seen as a problem of statistical bias. In fact, we prove that this effect leaves both individual interactions and their sample mean unbiased. Nevertheless, we show that it can make estimators inconsistent, introducing a distinct set of problems for convergence in relevance estimation. Our theoretical analysis investigates the conditions under which the bandwagon effect poses a consistency problem and explores several approaches for mitigating these issues. This work aims to show that the bandwagon effect poses an underinvestigated open problem that is fundamentally distinct from the well-studied selection bias in recommendation.