Fatigue-Aware Bandits for Dependent Click Models

Author:

Cao Junyu,Sun Wei,Shen Zuo-Jun (Max),Ettl Markus

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.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimizing recommendations under abandonment risks: Models and algorithms;Performance Evaluation;2023-09

2. FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation;Database Systems for Advanced Applications;2023

3. Multi-granularity Fatigue in Recommendation;Proceedings of the 31st ACM International Conference on Information & Knowledge Management;2022-10-17

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