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Multi-Task Recommendations with Reinforcement Learning

Authors:

Kun GaiAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 1273 - 1282

https://doi.org/10.1145/3543507.3583467

Published: 30 April 2023 Publication History

Abstract

In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications [40]. However, current MTL-based recommendation models tend to disregard the session-wise patterns of user-item interactions because they are predominantly constructed based on item-wise datasets. Moreover, balancing multiple objectives has always been a challenge in this field, which is typically avoided via linear estimations in existing works. To address these issues, in this paper, we propose a Reinforcement Learning (RL) enhanced MTL framework, namely RMTL, to combine the losses of different recommendation tasks using dynamic weights. To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks. Experiments on two real-world public datasets demonstrate the effectiveness of RMTL with a higher AUC against state-of-the-art MTL-based recommendation models. Additionally, we evaluate and validate RMTL’s compatibility and transferability across various MTL models.

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Cited By

Jia PWang YLin SLi XZhao XGuo HTang RWooldridge MDy JNatarajan S(2024)D3Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i8.28699(8553-8561)Online publication date: 20-Feb-2024
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Yu JLyu SChen P(2024)Deep Reinforcement Learning for Boosting Individual and Aggregate Diversity in Product Recommendation SystemsProceeding of the 2024 5th International Conference on Computer Science and Management Technology10.1145/3708036.3708043(39-48)Online publication date: 18-Oct-2024
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Sun YJi YZhu HZhuang FHe QXiong H(2024)Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement LearningACM Transactions on Information Systems10.1145/370499843:2(1-35)Online publication date: 21-Nov-2024
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Index Terms

Multi-Task Recommendations with Reinforcement Learning
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

Copyright © 2023 ACM.

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Published: 30 April 2023

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APRC - CityU New Research Initiatives
Ant Group (CCF-Ant Research Fund)
Huawei Innovation Research Program
SIRG - CityU Strategic Interdisciplinary Research Grant
HKIDS Early Career Research Grant

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WWW '23

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SIGWEB

WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

TX, Austin, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Jia PWang YLin SLi XZhao XGuo HTang RWooldridge MDy JNatarajan S(2024)D3Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i8.28699(8553-8561)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i8.28699
Yu JLyu SChen P(2024)Deep Reinforcement Learning for Boosting Individual and Aggregate Diversity in Product Recommendation SystemsProceeding of the 2024 5th International Conference on Computer Science and Management Technology10.1145/3708036.3708043(39-48)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3708036.3708043
Sun YJi YZhu HZhuang FHe QXiong H(2024)Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement LearningACM Transactions on Information Systems10.1145/370499843:2(1-35)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3704998
Han XZhu CHu XQin CZhao XZhu HBaeza-Yates RBonchi F(2024)Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671759(1004-1015)Online publication date: 25-Aug-2024
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Wang XLiu SWang XCai QHu LLi HJiang PGai KXie GBaeza-Yates RBonchi F(2024)Future Impact Decomposition in Request-level RecommendationsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671506(5905-5916)Online publication date: 25-Aug-2024
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Liu LWang WZhao XZhang ZZhang CLin SWang YZou LLiu ZWei XYin HLi QSerra ESpezzano F(2024)Efficient and Robust Regularized Federated RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679682(1452-1461)Online publication date: 21-Oct-2024
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Liu ZLiu SZhang ZCai QZhao XZhao KHu LJiang PGai KHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657829(1872-1882)Online publication date: 10-Jul-2024
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