  Exposure bias is a well-known issue in recommender systems where items and
suppliers are not equally represented in the recommendation results. This bias
becomes particularly problematic over time as a few items are repeatedly
over-represented in recommendation lists, leading to a feedback loop that
further amplifies this bias. Although extensive research has addressed this
issue in model-based or neighborhood-based recommendation algorithms, less
attention has been paid to online recommendation models, such as those based on
top-K contextual bandits, where recommendation models are dynamically updated
with ongoing user feedback. In this paper, we study exposure bias in a class of
well-known contextual bandit algorithms known as Linear Cascading Bandits. We
analyze these algorithms in their ability to handle exposure bias and provide a
fair representation of items in the recommendation results. Our analysis
reveals that these algorithms fail to mitigate exposure bias in the long run
during the course of ongoing user interactions. We propose an Exposure-Aware
reward model that updates the model parameters based on two factors: 1)
implicit user feedback and 2) the position of the item in the recommendation
list. The proposed model mitigates exposure bias by controlling the utility
assigned to the items based on their exposure in the recommendation list. Our
experiments with two real-world datasets show that our proposed reward model
improves the exposure fairness of the linear cascading bandits over time while
maintaining the recommendation accuracy. It also outperforms the current
baselines. Finally, we prove a high probability upper regret bound for our
proposed model, providing theoretical guarantees for its performance.
