The Bandwagon Effect: Not Just Another Bias
- URL: http://arxiv.org/abs/2206.12701v1
- Date: Sat, 25 Jun 2022 17:24:35 GMT
- Title: The Bandwagon Effect: Not Just Another Bias
- Authors: Norman Knyazev and Harrie Oosterhuis
- Abstract summary: We argue that the bandwagon effect should not be seen as a problem of statistical bias.
We show that it can make estimators inconsistent, introducing a distinct set of problems for convergence in relevance estimation.
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.
- Score: 13.579420996461439
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference [50.95521705711802]
Previous studies have focused on addressing selection bias to achieve unbiased learning of the prediction model.
This paper formally formulates the neighborhood effect as an interference problem from the perspective of causal inference.
We propose a novel ideal loss that can be used to deal with selection bias in the presence of neighborhood effect.
arXiv Detail & Related papers (2024-04-30T15:20:41Z) - Robustly Improving Bandit Algorithms with Confounded and Selection
Biased Offline Data: A Causal Approach [18.13887411913371]
This paper studies bandit problems where an agent has access to offline data that might be utilized to potentially improve the estimation of each arm's reward distribution.
We categorize the biases into confounding bias and selection bias based on the causal structure they imply.
We extract the causal bound for each arm that is robust towards compound biases from biased observational data.
arXiv Detail & Related papers (2023-12-20T03:03:06Z) - Causal Inference from Text: Unveiling Interactions between Variables [20.677407402398405]
Existing methods only account for confounding covariables that affect both treatment and outcome.
This bias arises from insufficient consideration of non-confounding covariables.
In this work, we aim to mitigate the bias by unveiling interactions between different variables.
arXiv Detail & Related papers (2023-11-09T11:29:44Z) - Approximating Counterfactual Bounds while Fusing Observational, Biased
and Randomised Data Sources [64.96984404868411]
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies.
We show that the likelihood of the available data has no local maxima.
We then show how the same approach can address the general case of multiple datasets.
arXiv Detail & Related papers (2023-07-31T11:28:24Z) - Fair Effect Attribution in Parallel Online Experiments [57.13281584606437]
A/B tests serve the purpose of reliably identifying the effect of changes introduced in online services.
It is common for online platforms to run a large number of simultaneous experiments by splitting incoming user traffic randomly.
Despite a perfect randomization between different groups, simultaneous experiments can interact with each other and create a negative impact on average population outcomes.
arXiv Detail & Related papers (2022-10-15T17:15:51Z) - Causal Intervention for Fairness in Multi-behavior Recommendation [40.938727601434195]
We argue that the relationships between different user behaviors (e.g., conversion rate) actually reflect the item quality.
To handle the unfairness issues, we propose to mitigate the popularity bias by considering multiple user behaviors.
arXiv Detail & Related papers (2022-09-10T04:21:25Z) - What's the Harm? Sharp Bounds on the Fraction Negatively Affected by
Treatment [58.442274475425144]
We develop a robust inference algorithm that is efficient almost regardless of how and how fast these functions are learned.
We demonstrate our method in simulation studies and in a case study of career counseling for the unemployed.
arXiv Detail & Related papers (2022-05-20T17:36:33Z) - Cross Pairwise Ranking for Unbiased Item Recommendation [57.71258289870123]
We develop a new learning paradigm named Cross Pairwise Ranking (CPR)
CPR achieves unbiased recommendation without knowing the exposure mechanism.
We prove in theory that this way offsets the influence of user/item propensity on the learning.
arXiv Detail & Related papers (2022-04-26T09:20:27Z) - Deep Causal Reasoning for Recommendations [47.83224399498504]
A new trend in recommender system research is to negate the influence of confounders from a causal perspective.
We model the recommendation as a multi-cause multi-outcome (MCMO) inference problem.
We show that MCMO modeling may lead to high variance due to scarce observations associated with the high-dimensional causal space.
arXiv Detail & Related papers (2022-01-06T15:00:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.