research-article

Causal factorization machine for robust recommendation

Authors:

Yongfeng ZhangAuthors Info & Claims

JCDL '22: Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries

Article No.: 10, Pages 1 - 9

https://doi.org/10.1145/3529372.3530921

Published: 20 June 2022 Publication History

Abstract

Factorization Machines (FMs) are widely used for the collaborative recommendation because of their effectiveness and flexibility in feature interaction modeling. Previous FM-based works have claimed the importance of selecting useful features since incorporating unnecessary features will introduce noise and reduce the recommendation performance. However, previous feature selection algorithms for FMs are proposed based on the i.i.d. hypothesis and select features according to their importance to the predictive accuracy on training data. However, the i.i.d. assumption is often violated in real-world applications, and shifts between training and testing sets may exist. In this paper, we consider achieving causal feature selection in FMs so as to enhance the robustness of recommendation when the distributions of training data and testing data are different. What's more, different from other machine learning tasks like image classification, which usually select a global set of causal features for a predictive model, we emphasize the importance of considering personalized causal feature selection in recommendation scenarios since the causal features for different users may be different. To achieve our goal, we propose a personalized feature selection method for FMs and refer to the confounder balancing approach to balance the confounders for every treatment feature. We conduct experiments on three real-world datasets and compare our method with both representative shallow and deep FM-based baselines to show the effectiveness of our method in enhancing the robustness of recommendations and improving the recommendation accuracy.

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Amara AHadj Taieb MBen Aouicha M(2025)A multi-view GNN-based network representation learning framework for recommendation systemsNeurocomputing10.1016/j.neucom.2024.129001619(129001)Online publication date: Feb-2025
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Li MCandan KSapino M(2024)Causally Informed Factorization Machines2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825754(448-455)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825754
Li YChen HXu SGe YTan JLiu SZhang Y(2023)Fairness in Recommendation: Foundations, Methods, and ApplicationsACM Transactions on Intelligent Systems and Technology10.1145/361030214:5(1-48)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3610302
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Index Terms

Causal factorization machine for robust recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

JCDL '22: Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries

June 2022

392 pages

ISBN:9781450393454

DOI:10.1145/3529372

General Chairs:
Akiko Aizawa
National Institute of Informatics, Japan
,
Thomas Mandl
University of Hildesheim, Germany
,
Zeljko Carevic
GESIS - Leibniz Institute for the Social Sciences, Germany
,
Program Chairs:
Annika Hinze
University of Waikato, New Zealand
,
Philipp Mayr
GESIS - Leibniz Institute for the Social Sciences, Germany
,
Philipp Schaer
TH Köln (University of Applied Sciences), Germany

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Published: 20 June 2022

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JCDL '22: The ACM/IEEE Joint Conference on Digital Libraries in 2022

June 20 - 24, 2022

Cologne, Germany

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JCDL '22 Paper Acceptance Rate 35 of 132 submissions, 27%;

Overall Acceptance Rate 415 of 1,482 submissions, 28%

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Cited By

Amara AHadj Taieb MBen Aouicha M(2025)A multi-view GNN-based network representation learning framework for recommendation systemsNeurocomputing10.1016/j.neucom.2024.129001619(129001)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129001
Li MCandan KSapino M(2024)Causally Informed Factorization Machines2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825754(448-455)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825754
Li YChen HXu SGe YTan JLiu SZhang Y(2023)Fairness in Recommendation: Foundations, Methods, and ApplicationsACM Transactions on Intelligent Systems and Technology10.1145/361030214:5(1-48)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3610302
Xu STan JHeinecke SLi VZhang Y(2023)Deconfounded Causal Collaborative FilteringACM Transactions on Recommender Systems10.1145/36060351:4(1-25)Online publication date: 3-Oct-2023
https://dl.acm.org/doi/10.1145/3606035
Zhang YShi TFeng FWang WWang DHe XZhang YChen HDuh WHuang HKato MMothe JPoblete B(2023)Reformulating CTR Prediction: Learning Invariant Feature Interactions for RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591755(1386-1395)Online publication date: 19-Jul-2023
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Wang CYe YMa LLi DZhuang L(2023)Dual disentanglement of user–item interaction for recommendation with causal embeddingInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10345660:5Online publication date: 1-Sep-2023
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Yang MZhang XWang JZhou X(2023)Causal representation for few-shot text classificationApplied Intelligence10.1007/s10489-023-04667-553:18(21422-21432)Online publication date: 3-Jun-2023
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Zhu YMa JLi J(2023)Causal Inference and RecommendationsMachine Learning for Causal Inference10.1007/978-3-031-35051-1_10(207-245)Online publication date: 9-Aug-2023
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Chen HLi YZhu HZhang YAl Hasan MXiong L(2022)Learn Basic Skills and ReuseProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557385(169-179)Online publication date: 17-Oct-2022
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Xu STan JFu ZJi JHeinecke SZhang YAl Hasan MXiong L(2022)Dynamic Causal Collaborative FilteringProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557300(2301-2310)Online publication date: 17-Oct-2022
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