Hierarchical Contextual Uplift Bandits for Catalog Personalization
Anupam Agrawal, Rajesh Mohanty, Shamik Bhattacharjee, Abhimanyu Mittal

TL;DR
This paper introduces a Hierarchical Contextual Uplift Bandit framework for catalog personalization that adapts to dynamic environments, improving recommendation quality and revenue in fantasy sports platforms.
Contribution
The paper presents a novel hierarchical and uplift modeling approach for contextual bandits, enabling better adaptation and transfer in rapidly changing environments.
Findings
0.4% revenue increase in A/B testing
Improved user satisfaction metrics
Additional 0.5% revenue boost after deployment
Abstract
Contextual Bandit (CB) algorithms are widely adopted for personalized recommendations but often struggle in dynamic environments typical of fantasy sports, where rapid changes in user behavior and dramatic shifts in reward distributions due to external influences necessitate frequent retraining. To address these challenges, we propose a Hierarchical Contextual Uplift Bandit framework. Our framework dynamically adjusts contextual granularity from broad, system-wide insights to detailed, user-specific contexts, using contextual similarity to facilitate effective policy transfer and mitigate cold-start issues. Additionally, we integrate uplift modeling principles into our approach. Results from large-scale A/B testing on the Dream11 fantasy sports platform show that our method significantly enhances recommendation quality, achieving a 0.4% revenue improvement while also improving user…
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Recommender Systems and Techniques · Data Stream Mining Techniques
