  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.
