{"id":27209,"date":"2021-06-25T21:25:23","date_gmt":"2021-06-25T13:25:23","guid":{"rendered":"https:\/\/swarma.org\/?p=27209"},"modified":"2021-06-25T21:25:23","modified_gmt":"2021-06-25T13:25:23","slug":"%e5%91%a8%e6%97%a5%e7%9b%b4%e6%92%ad%ef%bc%9a%e5%9f%ba%e4%ba%8e%e5%90%8e%e9%97%a8%e8%b0%83%e6%95%b4%e7%9a%84%e5%9b%a0%e6%9e%9c%e6%8e%a8%e8%8d%90%e7%b3%bb%e7%bb%9f-%e5%9b%a0%e6%9e%9c%e7%a7%91","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=27209","title":{"rendered":"\u5468\u65e5\u76f4\u64ad\uff1a\u57fa\u4e8e\u540e\u95e8\u8c03\u6574\u7684\u56e0\u679c\u63a8\u8350\u7cfb\u7edf | \u56e0\u679c\u79d1\u5b66\u8bfb\u4e66\u4f1a"},"content":{"rendered":"
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\u53c2\u8003\u6587\u732e\uff1a<\/span><\/strong><\/span><\/section>\n
1. Wang, W., Feng, F., He, X., Wang, X., & Chua, T. S. Deconfounded Recommendation for Alleviating Bias Amplification. In KDD, 2021.<\/span><\/section>\n
2. Wang, W., Feng, F., He, X., Zhang, H., & Chua, T. S. Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue. In SIGIR 2021.<\/span><\/section>\n
3. Zhang, Y., Feng, F., He, X., Wei, T., Song, C., Ling, G., & Zhang, Y. Causal Intervention for Leveraging Popularity Bias in Recommendation. In SIGIR 2021.<\/span><\/section>\n
4. Feng, F., Zhang, J., He, X., Zhang, H., & Chua, T. S. Empowering Language Understanding with Counterfactual Reasoning. In ACL, 2021.<\/span><\/section>\n
5. Wei, T., Feng, F., Chen, J., Shi, C., Wu, Z., Yi, J. and He, X. Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System. In KDD, 2021.<\/span><\/section>\n

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