Design Principles of Robust Multi-Armed Bandit Framework in Video Recommendations

TL;DR

This paper introduces new design principles for multi-armed bandit frameworks in video recommendation systems to enhance robustness against distributional shifts, item cannibalization, and data sparsity, leading to improved recommendation accuracy.

Contribution

The paper proposes novel design principles for robust bandit models in recommender systems, addressing challenges like dynamic signals, popularity bias, and data sparsity, which were underexplored in prior work.

Findings

Achieved up to 11.88% ROC-AUC improvement

Achieved up to 44.85% PR-AUC improvement

Demonstrated robustness in fairness and bias mitigation

Abstract

Current multi-armed bandit approaches in recommender systems (RS) have focused more on devising effective exploration techniques, while not adequately addressing common exploitation challenges related to distributional changes and item cannibalization. Little work exists to guide the design of robust bandit frameworks that can address these frequent challenges in RS. In this paper, we propose a new design principles to (i) make bandit models robust to time-variant metadata signals, (ii) less prone to item cannibalization, and (iii) prevent their weights fluctuating due to data sparsity. Through a series of experiments, we systematically examine the influence of several important bandit design choices. We demonstrate the advantage of our proposed design principles at making bandit models robust to dynamic behavioral changes through in-depth analyses. Noticeably, we show improved relative gain compared to a baseline bandit model not incorporating our design choices of up to $11.88\%$ and $44.85\%$, respectively in ROC-AUC and PR-AUC. Case studies about fairness in recommending specific popular and unpopular titles are presented, to demonstrate the robustness of our proposed design at addressing popularity biases.