arXiv:2605.21066v1 Announce Type: new Abstract: Recommender systems often rely on observational user--item interaction data, which is prone to selection bias due to users' selective interactions with items. Inverse propensity weighting and doubly robust estimators effectively mitigate selection bias under observed confounding, but are unreliable in the presence of hidden confounders. Existing approaches relying on randomized controlled trials (RCTs) or global sensitivity bounds are constrained in practice: RCTs demand costly experimental data, while global sensitivity bounds presume a uniforml
The increasing sophistication and widespread deployment of recommender systems necessitate more robust methods for personalized recommendations, especially as these systems handle vast, often biased, observational data.
This research is crucial for improving the accuracy and fairness of AI-driven recommendations across various industries, directly impacting user experience and platform effectiveness without costly experimental setups.
The proposed method offers a way to achieve robust personalized recommendations even in the presence of hidden confounders, potentially reducing the reliance on expensive randomized controlled trials.
- · E-commerce platforms
- · Content streaming services
- · Adtech industry
- · AI/ML researchers
- · Companies with biased recommendation systems
- · Less robust causal inference methods dependent on observed confounders
More accurate and fair recommendations will enhance user satisfaction and engagement across digital platforms.
Improved recommendation efficacy could lead to higher conversion rates and revenue for businesses employing these systems.
Broader adoption of robust causal inference in AI could reduce ethical concerns related to algorithmic bias and discrimination.
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Read at arXiv cs.LG