The Application of Causal Inference Algorithms in Federated Recommender Systems
This study introduces the application of causal inference algorithms in federated recommender systems through a framework called FedCIRec. The framework utilizes two key causal inference methods: Instrumental Variable (IV) and Counterfactual Inference (CF). IV helps identify and address confounding...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10360120/ |
| _version_ | 1827338301490593792 |
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| author | Yuanming Ding Yongquan Sun Jianxin Feng |
| author_facet | Yuanming Ding Yongquan Sun Jianxin Feng |
| author_sort | Yuanming Ding |
| collection | DOAJ |
| description | This study introduces the application of causal inference algorithms in federated recommender systems through a framework called FedCIRec. The framework utilizes two key causal inference methods: Instrumental Variable (IV) and Counterfactual Inference (CF). IV helps identify and address confounding biases, improving recommendation accuracy. CF addresses data loss issues in federated learning, enabling the inference of possible scenarios and outcomes. Experimental results on the MIND and Adressa datasets demonstrate excellent performance in evaluation metrics. The FedCIRec framework has advantages over centralized storage and user privacy protection methods. Further exploration of causal inference methods and optimization of recommendation effects while protecting user privacy are areas for future research. Introducing causal inference techniques in federated recommendation systems has broad research and practical application prospects. |
| first_indexed | 2024-03-07T19:11:49Z |
| format | Article |
| id | doaj.art-f4cb8e95dfce48ed8040713801608bed |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T19:11:49Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f4cb8e95dfce48ed8040713801608bed2024-03-01T00:00:39ZengIEEEIEEE Access2169-35362024-01-0112297482975810.1109/ACCESS.2023.334286110360120The Application of Causal Inference Algorithms in Federated Recommender SystemsYuanming Ding0https://orcid.org/0000-0002-8958-1176Yongquan Sun1https://orcid.org/0009-0008-7647-943XJianxin Feng2https://orcid.org/0000-0002-3780-6863Communication and Network Key Laboratory, Dalian University, Dalian, ChinaCommunication and Network Key Laboratory, Dalian University, Dalian, ChinaCommunication and Network Key Laboratory, Dalian University, Dalian, ChinaThis study introduces the application of causal inference algorithms in federated recommender systems through a framework called FedCIRec. The framework utilizes two key causal inference methods: Instrumental Variable (IV) and Counterfactual Inference (CF). IV helps identify and address confounding biases, improving recommendation accuracy. CF addresses data loss issues in federated learning, enabling the inference of possible scenarios and outcomes. Experimental results on the MIND and Adressa datasets demonstrate excellent performance in evaluation metrics. The FedCIRec framework has advantages over centralized storage and user privacy protection methods. Further exploration of causal inference methods and optimization of recommendation effects while protecting user privacy are areas for future research. Introducing causal inference techniques in federated recommendation systems has broad research and practical application prospects.https://ieeexplore.ieee.org/document/10360120/Federated learningcausal inferencerecommendation systemsprivacy protection |
| spellingShingle | Yuanming Ding Yongquan Sun Jianxin Feng The Application of Causal Inference Algorithms in Federated Recommender Systems IEEE Access Federated learning causal inference recommendation systems privacy protection |
| title | The Application of Causal Inference Algorithms in Federated Recommender Systems |
| title_full | The Application of Causal Inference Algorithms in Federated Recommender Systems |
| title_fullStr | The Application of Causal Inference Algorithms in Federated Recommender Systems |
| title_full_unstemmed | The Application of Causal Inference Algorithms in Federated Recommender Systems |
| title_short | The Application of Causal Inference Algorithms in Federated Recommender Systems |
| title_sort | application of causal inference algorithms in federated recommender systems |
| topic | Federated learning causal inference recommendation systems privacy protection |
| url | https://ieeexplore.ieee.org/document/10360120/ |
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