Fatigue-aware Bandits for Dependent Click Models

TL;DR

This paper introduces a fatigue-aware extension of the Dependent Click Model for recommender systems, enabling online learning algorithms to adapt recommendations considering user fatigue effects, with proven regret bounds.

Contribution

It develops a novel fatigue-aware DCM bandit model and proposes algorithms for both known and unknown discounting effects, with theoretical regret analysis.

Findings

Proposed algorithms effectively learn user preferences considering fatigue.

Achieved regret bounds for both scenarios of known and unknown discounting.

Demonstrated improved recommendation strategies accounting for user fatigue.

Abstract

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.