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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Fatigue-Aware Bandits for Dependent Click Models

February 1, 2023

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Authors

Junyu Cao

University of California Berkeley


Wei Sun

IBM Research


Zuo-Jun (Max) Shen

University of California Berkeley


Markus Ettl

IBM Research


DOI:

10.1609/aaai.v34i04.5735


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.

Topics: AAAI

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HOW TO CITE:

Junyu Cao||Wei Sun||Zuo-Jun (Max) Shen||Markus Ettl Fatigue-Aware Bandits for Dependent Click Models Proceedings of the AAAI Conference on Artificial Intelligence (2020) 3341-3348.

Junyu Cao||Wei Sun||Zuo-Jun (Max) Shen||Markus Ettl Fatigue-Aware Bandits for Dependent Click Models AAAI 2020, 3341-3348.

Junyu Cao||Wei Sun||Zuo-Jun (Max) Shen||Markus Ettl (2020). Fatigue-Aware Bandits for Dependent Click Models. Proceedings of the AAAI Conference on Artificial Intelligence, 3341-3348.

Junyu Cao||Wei Sun||Zuo-Jun (Max) Shen||Markus Ettl. Fatigue-Aware Bandits for Dependent Click Models. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.3341-3348.

Junyu Cao||Wei Sun||Zuo-Jun (Max) Shen||Markus Ettl. 2020. Fatigue-Aware Bandits for Dependent Click Models. "Proceedings of the AAAI Conference on Artificial Intelligence". 3341-3348.

Junyu Cao||Wei Sun||Zuo-Jun (Max) Shen||Markus Ettl. (2020) "Fatigue-Aware Bandits for Dependent Click Models", Proceedings of the AAAI Conference on Artificial Intelligence, p.3341-3348

Junyu Cao||Wei Sun||Zuo-Jun (Max) Shen||Markus Ettl, "Fatigue-Aware Bandits for Dependent Click Models", AAAI, p.3341-3348, 2020.

Junyu Cao||Wei Sun||Zuo-Jun (Max) Shen||Markus Ettl. "Fatigue-Aware Bandits for Dependent Click Models". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.3341-3348.

Junyu Cao||Wei Sun||Zuo-Jun (Max) Shen||Markus Ettl. "Fatigue-Aware Bandits for Dependent Click Models". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 3341-3348.

Junyu Cao||Wei Sun||Zuo-Jun (Max) Shen||Markus Ettl. Fatigue-Aware Bandits for Dependent Click Models. AAAI[Internet]. 2020[cited 2023]; 3341-3348.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
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101, Palo Alto, California 94303 All Rights Reserved

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