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Recommender systems usually learn user interests from various user behaviors, including clicks and post-click behaviors (e.g., like and favorite).
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W. Zhang, W. Bao, X.-Y. Liu, K. Yang, Q. Lin, H. Wen, and R. Ramezani, “Large-scale causal approaches to debiasizaing post-click conversion rate estimation with multi-task learning,” in Proceedings of The Web Conference 2020 , 2020, pp. 2775–2781
2020
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H. Wen, J. Zhang, Y. Wang, F. Lv, W. Bao, Q. Lin, and K. Yang, “Entire space multi-task modeling via post-click behavior decomposition for conversion rate prediction,” in Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval , 2020, pp. 2377–2386
2020
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Y. Saito, S. Yaginuma, Y. Nishino, H. Sakata, and K. Nakata, “Unbiased recommender learning from missing-not-at-random implicit feedback,” in Proceedings of the International Conference on Web Search and Data Mining . ACM, 2020, pp. 501–509
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M. Morik, A. Singh, J. Hong, and T. Joachims, “Controlling fairness and bias in dynamic learning-to-rank,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval , 2020, pp. 429–438
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Y. Ge, S. Zhao, H. Zhou, C. Pei, F. Sun, W. Ou, and Y. Zhang, “Understanding echo chambers in e-commerce recommender systems,” in Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval , 2020, pp. 2261–2270
2020
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Z. Zhu, Y. He, Y. Zhang, and J. Caverlee, “Unbiased implicit recommendation and propensity estimation via combinational joint learning,” in Fourteenth ACM Conference on Recommender Systems , 2020, p. 551–556
2020
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2021
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W. Wang, F. Feng, X. He, H. Zhang, and T.-S. Chua, “Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2021, pp. 1288–1297
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Y. Zheng, C. Gao, X. Li, X. He, Y. Li, and D. Jin, “Disentangling user interest and conformity for recommendation with causal embedding,” in Proceedings of the Web Conference 2021 , 2021, pp. 2980–2991
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R. Islam, K. N. Keya, Z. Zeng, S. Pan, and J. Foulds, “Debiasing career recommendations with neural fair collaborative filtering,” in Proceedings of the Web Conference 2021 , 2021, pp. 3779–3790
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Y. Ge, S. Liu, R. Gao, Y. Xian, Y. Li, X. Zhao, C. Pei, F. Sun, J. Ge, W. Ou et al. , “Towards long-term fairness in recommendation,” in Proceedings of the 14th ACM International Conference on Web Search and Data Mining , 2021, pp. 445–453
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Y. Li, H. Chen, Z. Fu, Y. Ge, and Y. Zhang, “User-oriented fairness in recommendation,” in Proceedings of the Web Conference 2021 , 2021, pp. 624–632
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G. Xv, C. Lin, H. Li, J. Su, W. Ye, and Y. Chen, “Neutralizing popularity bias in recommendation models,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 2623–2628
2022
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Z. Chen, J. Wu, C. Li, J. Chen, R. Xiao, and B. Zhao, “Co-training disentangled domain adaptation network for leveraging popularity bias in recommenders,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 60–69
2022
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Y. He, Z. Wang, P. Cui, H. Zou, Y. Zhang, Q. Cui, and Y. Jiang, “Causpref: Causal preference learning for out-of-distribution recommendation,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 410–421
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Z. Si, X. Han, X. Zhang, J. Xu, Y. Yin, Y. Song, and J.-R. Wen, “A model-agnostic causal learning framework for recommendation using search data,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 224–233
2022
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2022
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R. Xu, X. Zhang, P. Cui, B. Li, Z. Shen, and J. Xu, “Regulatory instruments for fair personalized pricing,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 4–15
2022
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J. Li, Y. Ren, and K. Deng, “Fairgan: Gans-based fairness-aware learning for recommendations with implicit feedback,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 297–307
2022
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Z. He, B. Hui, S. Zhang, C. Xiao, T. Zhong, and F. Zhou, “Exploring indirect entity relations for knowledge graph enhanced recommender system,” Expert Systems with Applications , vol. 213, p. 118984, 2023
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