  Exposure bias is a well-known issue in recommender systems where items and
suppliers are not equally represented in the recommendation results. This is
especially problematic when bias is amplified over time as a few popular items
are repeatedly over-represented in recommendation lists. This phenomenon can be
viewed as a recommendation feedback loop: the system repeatedly recommends
certain items at different time points and interactions of users with those
items will amplify bias towards those items over time. This issue has been
extensively studied in the literature on model-based or neighborhood-based
recommendation algorithms, but less work has been done on online recommendation
models such as those based on multi-armed Bandit algorithms. In this paper, we
study exposure bias in a class of well-known bandit algorithms known as Linear
Cascade Bandits. We analyze these algorithms on their ability to handle
exposure bias and provide a fair representation for items and suppliers in the
recommendation results. Our analysis reveals that these algorithms fail to
treat items and suppliers fairly and do not sufficiently explore the item space
for each user. To mitigate this bias, we propose a discounting factor and
incorporate it into these algorithms that controls the exposure of items at
each time step. To show the effectiveness of the proposed discounting factor on
mitigating exposure bias, we perform experiments on two datasets using three
cascading bandit algorithms and our experimental results show that the proposed
method improves the exposure fairness for items and suppliers.
