  As recommender systems send a massive amount of content to keep users
engaged, users may experience fatigue which is contributed by 1) an
overexposure to irrelevant content, 2) boredom from seeing too many similar
recommendations. To address this problem, we consider an online learning
setting where a platform learns a policy to recommend content that takes user
fatigue into account. We propose an extension of the Dependent Click Model
(DCM) to describe users' behavior. We stipulate that for each piece of content,
its attractiveness to a user depends on its intrinsic relevance and a discount
factor which measures how many similar contents have been shown. Users view the
recommended content sequentially and click on the ones that they find
attractive. Users may leave the platform at any time, and the probability of
exiting is higher when they do not like the content. Based on user's feedback,
the platform learns the relevance of the underlying content as well as the
discounting effect due to content fatigue. We refer to this learning task as
"fatigue-aware DCM Bandit" problem. We consider two learning scenarios
depending on whether the discounting effect is known. For each scenario, we
propose a learning algorithm which simultaneously explores and exploits, and
characterize its regret bound.
