{"id":37215,"date":"2022-09-10T17:07:20","date_gmt":"2022-09-10T09:07:20","guid":{"rendered":"https:\/\/swarma.org\/?p=37215"},"modified":"2022-09-10T17:07:20","modified_gmt":"2022-09-10T09:07:20","slug":"%e5%9b%a0%e6%9e%9c%e6%8e%a8%e8%8d%90%e7%b3%bb%e7%bb%9f%e5%89%8d%e6%b2%bf%e8%bf%9b%e5%b1%95%ef%bc%9a%e5%bd%a2%e5%bc%8f%e5%8c%96%e4%b8%8e%e5%8e%bb%e5%81%8f","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=37215","title":{"rendered":"\u56e0\u679c\u63a8\u8350\u7cfb\u7edf\u524d\u6cbf\u8fdb\u5c55\uff1a\u5f62\u5f0f\u5316\u4e0e\u53bb\u504f"},"content":{"rendered":"
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\u8fd1\u5e74\u6765\uff0c\u56e0\u679c\u63a8\u7406\u5f15\u8d77\u4e86\u5b66\u672f\u754c\u548c\u5de5\u4e1a\u754c\u7684\u5e7f\u6cdb\u5173\u6ce8\u3002\u5317\u4eac\u5927\u5b66\u5468\u6653\u534e\u6559\u6388\u56e2\u961f\u5728\u56e0\u679c\u63a8\u8350\u7cfb\u7edf\u9886\u57df\u9876\u5c16\u4f1a\u8bae\u53d1\u8868\u4e86\u591a\u7bc7\u8bba\u6587\uff0c\u63d0\u51fa\u4e86\u4e00\u5957\u56e0\u679c\u5206\u6790\u6846\u67b6\u6765\u7edf\u4e00\u73b0\u6709\u7684\u5404\u7c7b\u56e0\u679c\u542f\u53d1\u7684\u63a8\u8350\u65b9\u6cd5\uff0c\u5e76\u57fa\u4e8e\u6b64\u5728\u591a\u4e2a\u7ecf\u5178\u63a8\u8350\u573a\u666f\uff0c\u4f8b\u5982\uff1a\u9009\u62e9\u504f\u5dee\u3001\u70b9\u51fb\u540e\u8f6c\u5316\u7387\u9884\u6d4b\u3001\u9690\u53cd\u9988\u3001\u591a\u7a33\u5065\u5b66\u4e60\u3001\u7528\u6237\u4ece\u4f17\u504f\u5dee\u7b49\u591a\u4e2a\u573a\u666f\u4f7f\u7528\u56e0\u679c\u63a8\u65ad\u6280\u672f\uff0c\u5728\u7406\u8bba\u548c\u6570\u503c\u8bd5\u9a8c\u65b9\u9762\u5747\u5c55\u73b0\u51fa\u4e86\u5353\u8d8a\u7684\u6027\u80fd\u3002<\/span><\/section>\n

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1. \u300aOn the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges\u300b\uff08\u5317\u4eac\u5927\u5b66\uff0c\u534e\u4e3a\uff09<\/span><\/strong><\/span>
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\u63a8\u8350\u7cfb\u7edf\u4e2d\u7684\u56e0\u679c\u5206\u6790\u6846\u67b6<\/span><\/section>\n

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\u5728\u5317\u4eac\u5927\u5b66\u4e0e\u534e\u4e3a\u7684\u7814\u7a76\u6210\u679c\u300aOn the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges\u300b\u4e2d\uff0c\u6211\u4eec\u65e8\u5728\u901a\u8fc7\u5728\u56e0\u679c\u63a8\u65ad\u4e2d\u7684\u6f5c\u5728\u7ed3\u679c\u6846\u67b6\u6765\u514b\u670d\u4e0a\u8ff0\u9650\u5236\uff0c\u901a\u8fc7\u8be5\u6846\u67b6\u6211\u4eec\u7efc\u8ff0\u5e76\u7edf\u4e00\u4e86\u73b0\u6709\u7684\u56e0\u679c\u542f\u53d1\u7684\u63a8\u8350\u65b9\u6cd5\u3002\u6211\u4eec\u901a\u8fc7\u5206\u6790\u73b0\u6709\u7814\u7a76\u4e2d\u53ef\u80fd\u4f7f\u7528\u4f46\u672a\u8ba8\u8bba\u7684\u56e0\u679c\u5047\u8bbe\uff0c\u4e3a\u63a8\u8350\u7cfb\u7edf\u4e2d\u7684\u504f\u5dee\u63d0\u4f9b\u56e0\u679c\u89c6\u89d2\uff0c\u7136\u540e\u8ba8\u8bba\u8fdd\u53cd\u4e0a\u8ff0\u5047\u8bbe\u7684\u76f8\u5e94\u63a8\u8350\u573a\u666f\u3002\u4e8b\u5b9e\u4e0a\uff0c\u5927\u91cf\u7684\u63a8\u8350\u4efb\u52a1\u901a\u8fc7\u5e94\u7528\u63d0\u51fa\u7684\u56e0\u679c\u6846\u67b6\u5f97\u5230\u4e86\u4e25\u683c\u7684\u6f84\u6e05\u3002\u6700\u540e\uff0c\u6211\u4eec\u5bf9\u63a8\u8350\u7cfb\u7edf\u4e2d\u7684\u8bb8\u591a\u53bb\u504f\u548c\u9884\u6d4b\u4efb\u52a1\u8fdb\u884c\u4e86\u5f62\u5f0f\u5316\uff0c\u5e76\u603b\u7ed3\u4e86\u57fa\u4e8e\u7edf\u8ba1\u548c\u673a\u5668\u5b66\u4e60\u7684\u56e0\u679c\u4f30\u8ba1\u65b9\u6cd5\uff0c\u671f\u671b\u4e3a\u56e0\u679c\u63a8\u8350\u7cfb\u7edf\u793e\u533a\u63d0\u4f9b\u65b0\u7684\u7814\u7a76\u673a\u4f1a\u548c\u89c6\u89d2\u3002<\/span><\/section>\n

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\u3010\u63a8\u8350\u7cfb\u7edf\u56e0\u679c\u89c6\u89d2\u7684\u504f\u5dee\u5f62\u5f0f\u5316\u3011<\/span><\/section>\n
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2.\u300aA Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction\u300b\uff08\u5317\u4eac\u5927\u5b66\uff0c\u534e\u4e3a\uff09<\/span><\/strong>
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\u70b9\u51fb\u540e\u8f6c\u5316\u7387<\/span>\uff08CVR\uff09<\/span>\u7684\u9884\u6d4b\u5728\u73b0\u4ee3\u63a8\u8350\u7cfb\u7edf\u4e2d\u5f97\u5230\u4e86\u5e7f\u6cdb\u7684\u5173\u6ce8\uff0c\u56e0\u4e3a\u70b9\u51fb\u540e\u8f6c\u5316\u7684\u53cd\u9988\u5305\u542b\u4e86\u7528\u6237\u504f\u597d\u7684\u5f3a\u70c8\u4fe1\u53f7\uff0c\u5e76\u76f4\u63a5\u4fc3\u8fdb\u4e86\u5546\u54c1\u603b\u91cf<\/span>\uff08GMV\uff09<\/span>\u3002\u5728\u8bb8\u591a\u5de5\u4e1a\u5e94\u7528\u4e2d\uff0cCVR\u9884\u6d4b\u901a\u5e38\u88ab\u8ba4\u4e3a\u662f\u53d1\u73b0\u7528\u6237\u5174\u8da3\u548c\u589e\u52a0\u5e73\u53f0\u6536\u5165\u7684\u6838\u5fc3\u4efb\u52a1\u3002\u5bf9\u4e8e\u4e00\u4e2a\u7528\u6237-\u7269\u54c1\u5bf9\uff0cCVR\u4ee3\u8868\u4e86\u7528\u6237\u5728\u70b9\u51fb\u8be5\u7269\u54c1\u540e\u6d88\u8d39\u8be5\u7269\u54c1\u7684\u6982\u7387\u3002\u4ece\u672c\u8d28\u4e0a\u8bb2\uff0cCVR\u9884\u6d4b\u7684\u4efb\u52a1\u662f\u4e00\u4e2a\u53cd\u4e8b\u5b9e\u7684\u95ee\u9898\u3002\u8fd9\u662f\u56e0\u4e3a\u5728\u63a8\u7406\u8fc7\u7a0b\u4e2d\uff0c\u6211\u4eec\u60f3\u77e5\u9053\u7684\u672c\u8d28\u4e0a\u662f\u6240\u6709\u7528\u6237-\u9879\u76ee\u5bf9\u7684\u8f6c\u6362\u7387\uff0c\u5047\u8bbe\u6240\u6709\u9879\u76ee\u90fd\u88ab\u6240\u6709\u7528\u6237\u70b9\u51fb\uff0c\u8fd9\u662f\u4e00\u4e2a\u4e0e\u73b0\u5b9e\u76f8\u77db\u76fe\u7684\u5047\u8bbe\u60c5\u51b5\u3002<\/span><\/section>\n

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\u5bf9\u4e8e\u8003\u8651\u9009\u62e9\u504f\u5dee\u7684CVR\u7684\u65e0\u504f\u4f30\u8ba1\u5668\uff1a\u57fa\u4e8e\u8bef\u5dee\u63a8\u65ad<\/span>\uff08EIB\uff09<\/span>\u548c\u9006\u6982\u7387\u52a0\u6743<\/span>\uff08IPS\uff09<\/span>\u662fCVR\u9884\u6d4b\u53bb\u504f\u7684\u4e24\u4e2a\u4e3b\u8981\u7b56\u7565\u3002\u6b64\u5916\uff0c\u53ef\u4ee5\u901a\u8fc7\u7ed3\u5408EIB\u548cIPS\u65b9\u6cd5\u6784\u5efa\u53cc\u7a33\u5065<\/span>\uff08DR\uff09<\/span>\u4f30\u8ba1\u5668\u3002\u4e00\u4e2aDR\u4f30\u8ba1\u5668\u5c06\u5177\u6709\u53cc\u7a33\u5065\u7684\u7279\u6027\uff0c\u5982\u679c\u63a8\u65ad\u8bef\u5dee\u6216\u503e\u5411\u6027\u5f97\u5206\u90fd\u662f\u51c6\u786e\u7684\uff0c\u5b83\u53ef\u4ee5\u4fdd\u8bc1\u5bf9CVR\u7684\u65e0\u504f\u4f30\u8ba1\u3002\u4e0eEIB\u548cIPS\u65b9\u6cd5\u76f8\u6bd4\uff0cDR\u65b9\u6cd5\u5728\u603b\u4f53\u4e0a\u6709\u66f4\u597d\u7684\u8868\u73b0\u3002<\/span><\/section>\n

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\u5c3d\u7ba1DR\u65b9\u6cd5\u901a\u5e38\u4e0eEIB\u548cIPS\u4f30\u8ba1\u5668\u76f8\u6bd4\u66f4\u6709\u4f18\u52bf\uff0c\u4f46\u4ecd\u6709\u4e00\u4e9b\u62c5\u5fe7\u3002\u901a\u8fc7\u5bf9DR\u4f30\u8ba1\u5668\u7684\u7406\u8bba\u5206\u6790\u8868\u660e\uff1a\u504f\u5dee\u3001\u65b9\u5dee\u548c\u6cdb\u5316\u754c\u90fd\u53d6\u51b3\u4e8e\u7531\u503e\u5411\u5f97\u5206\u7684\u5012\u6570\u52a0\u6743\u7684\u8bef\u5dee\u63a8\u65ad\u6a21\u578b\u7684\u8bef\u5dee\u504f\u79bb\u3002\u8fd9\u662f\u4e00\u4e2a\u4ee4\u4eba\u62c5\u5fe7\u7684\u7ed3\u679c\uff0c\u56e0\u4e3a\u5728\u672a\u70b9\u51fb\u7684\u4e8b\u4ef6\u4e2d\uff0c\u503e\u5411\u4e8e\u4f7f\u7528\u5927\u7684\u503e\u5411\u6027\u52a0\u6743\uff0c\u800c\u7531\u4e8e\u9009\u62e9\u504f\u5dee\u548c\u6570\u636e\u7a00\u5c11\uff0c\u8bef\u5dee\u63a8\u65ad\u6a21\u578b\u7684\u8bef\u5dee\u504f\u5dee\u5728\u672a\u70b9\u51fb\u7684\u4e8b\u4ef6\u4e2d\u5f88\u53ef\u80fd\u662f\u4e0d\u51c6\u786e\u7684\u3002\u8fd9\u8868\u660e\uff0c\u5728\u672a\u70b9\u51fb\u4e8b\u4ef6\u4e2d\uff0c\u5728\u4e0d\u51c6\u786e\u7684\u63a8\u65ad\u8bef\u5dee\u4e0b\uff0c\u504f\u5dee\u3001\u65b9\u5dee\u548c\u6cdb\u5316\u8fb9\u754c\u53ef\u80fd\u4ecd\u7136\u5f88\u5927\u3002\u6700\u8fd1\uff0c\u4e00\u4e9b\u65b9\u6cd5\uff0c\u4e3b\u8981\u5305\u62ec\u53cc\u91cd\u7a33\u5065\u8054\u5408\u5b66\u4e60<\/span>\uff08DR-JL\uff09<\/span>\u548c\u66f4\u52a0\u7a33\u5065\u7684\u53cc\u91cd\u7a33\u5065<\/span>\uff08MRDR\uff09<\/span>\uff0c\u5df2\u7ecf\u88ab\u8bbe\u8ba1\u7528\u6765\u7f13\u89e3\u8fd9\u4e2a\u95ee\u9898\u3002MRDR\u65e8\u5728\u51cf\u5c11DR\u635f\u5931\u7684\u65b9\u5dee\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u9c81\u68d2\u6027\uff0c\u4f46\u5f53\u504f\u5dee\u8f83\u5927\u65f6\uff0c\u5b83\u4ecd\u7136\u53ef\u80fd\u6709\u8f83\u5dee\u7684\u6cdb\u5316\u6027\u3002DR-JL\u8bd5\u56fe\u51cf\u5c11\u8bef\u5dee\u63a8\u65ad\u6a21\u578b\u7684\u504f\u5dee\uff0c\u4ee5\u83b7\u5f97\u66f4\u51c6\u786e\u7684CVR\u4f30\u8ba1\u503c\uff0c\u4f46\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u80fd\u76f4\u63a5\u63a7\u5236\u504f\u5dee\u548c\u65b9\u5dee\u3002\u56e0\u6b64\uff0c\u5982\u679c\u6211\u4eec\u80fd\u627e\u5230\u4e00\u79cd\u66f4\u6709\u6548\u7684\u65b9\u6cd5\u6765\u76f4\u63a5\u63a7\u5236\u504f\u5dee\u548c\u65b9\u5dee\uff0c\u5c06\u4f1a\u5f88\u6709\u5e2e\u52a9\u3002<\/span><\/section>\n

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\u3010\u5404\u79cdDR\u65b9\u6cd5\u7684\u901a\u7528\u6846\u67b6\u3011<\/span><\/section>\n
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\u5728\u5317\u4eac\u5927\u5b66\u4e0e\u534e\u4e3a\u7684\u7814\u7a76\u6210\u679c\u300aA Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction\u300b\u4e2d\uff0c\u6211\u4eec\u63ed\u793a\u4e86CVR\u9884\u6d4b\u4efb\u52a1\u80cc\u540e\u7684\u53cd\u4e8b\u5b9e\u95ee\u9898\uff0c\u5e76\u7ed9\u51fa\u4e86CVR\u7684\u6b63\u5f0f\u548c\u4e25\u683c\u7684\u56e0\u679c\u5b9a\u4e49\u3002\u7136\u540e\uff0c\u901a\u8fc7\u5206\u6790DR\u4f30\u8ba1\u5668\u7684\u504f\u5dee\u3001\u65b9\u5dee\u548c\u6cdb\u5316\u754c\uff0c\u6211\u4eec\u63a8\u5bfc\u51fa\u4e00\u4e2a\u65b0\u9896\u7684\u6cdb\u5316\u5b66\u4e60\u6846\u67b6\uff0c\u8be5\u6846\u67b6\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u4e0d\u540c\u7684\u635f\u5931\u51fd\u6570\u6307\u6807\u6765\u9002\u5e94\u5e7f\u6cdb\u7684CVR\u4f30\u8ba1\u5668\u3002\u8fd9\u4e2a\u6846\u67b6\u7edf\u4e00\u4e86\u73b0\u6709\u7684\u5404\u79cd\u53cc\u7a33\u5065\u7684CVR\u9884\u6d4b\u65b9\u6cd5\uff0c\u5982DR-JL\u548cMRDR\u3002\u6700\u91cd\u8981\u7684\u662f\uff0c\u5b83\u4e3a\u8bbe\u8ba1\u65b0\u7684\u4f30\u8ba1\u5668\u4ee5\u9002\u5e94CVR\u9884\u6d4b\u4e2d\u4e0d\u540c\u7684\u5e94\u7528\u573a\u666f\u63d0\u4f9b\u4e86\u5173\u952e\u7684\u89c1\u89e3\u3002\u57fa\u4e8e\u8fd9\u4e00\u6846\u67b6\uff0c\u4ece\u504f\u5dee-\u65b9\u5dee\u6743\u8861\u7684\u89d2\u5ea6\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e24\u4e2a\u65b0\u7684\u53cc\u7a33\u5065\u4f30\u8ba1\u5668\uff0c\u79f0\u4e3aDR-BIAS\u548cDR-MSE\uff0c\u5176\u76ee\u7684\u662f\u66f4\u7075\u6d3b\u5730\u63a7\u5236DR\u635f\u5931\u51fd\u6570\u7684\u504f\u5dee\u548c\u5747\u65b9\u8bef\u5dee<\/span>\uff08MSE\uff09<\/span>\uff0c\u57fa\u4e8e\u6211\u4eec\u7684\u5206\u6790\uff0c\u4e0e\u73b0\u6709\u57fa\u4e8eDR\u7684\u65b9\u6cd5\u76f8\u6bd4\uff0cDR-MSE\u5b9e\u73b0\u4e86\u66f4\u597d\u7684\u6cdb\u5316\u6027\u80fd\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u4e09\u7ea7\u8054\u5408\u5b66\u4e60\u4f18\u5316\u65b9\u6cd5\uff0c\u7528\u4e8e\u5728CVR\u9884\u6d4b\u4e2d\u5b9e\u73b0\u7075\u6d3b\u7684DR-MSE\uff0c\u5e76\u63d0\u51fa\u4e86\u76f8\u5e94\u7684\u9ad8\u6548\u8bad\u7ec3\u7b97\u6cd5\u3002\u6211\u4eec\u8fdb\u884c\u4e86\u5927\u91cf\u7684\u5b9e\u9a8c\u6765\u9a8c\u8bc1\u6240\u63d0\u51fa\u7684\u65b9\u6cd5\u4e0e\u6700\u5148\u8fdb\u7684\u6280\u672f\u76f8\u6bd4\u7684\u4f18\u52bf\u3002\u5728\u5b9e\u9a8c\u4e2d\uff0cDR-MSE\u5728DCG@2\u4e2d\u4f18\u4e8e\u5b58\u5728\u7814\u7a76\u65b9\u6cd5\u76843.22%\u3002<\/span><\/section>\n

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\u3010\u57fa\u4e8eCoat\u548cYahoo\u7684\u6027\u80fd\u6bd4\u8f83\u3011<\/span><\/section>\n
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\u3010\u57fa\u4e8e\u5927\u89c4\u6a21App\u5e7f\u544a\u6570\u636e\u96c6\u7684\u6027\u80fd\u6bd4\u8f83\u3011<\/span><\/section>\n
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3.\u300aDoubly Robust Collaborative Targeted Learning for Debiased Recommendations\u300b\uff08\u5317\u4eac\u5927\u5b66\uff09<\/span>
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\u57fa\u4e8e\u56e0\u679c\u63a8\u65ad\u7684\u65b9\u6cd5\u6765\u89e3\u51b3\u63a8\u8350\u7cfb\u7edf\u4e2d\u7684\u5404\u79cd\u4efb\u52a1\u5df2\u7ecf\u53d8\u5f97\u8d8a\u6765\u8d8a\u6d41\u884c\u3002\u8fd9\u4e00\u8d8b\u52bf\u7684\u4e3b\u8981\u539f\u56e0\u4e4b\u4e00\u662f\uff0c\u63a8\u8350\u7cfb\u7edf\u4e2d\u7684\u8bb8\u591a\u5b9e\u9645\u4efb\u52a1\u672c\u8eab\u5c31\u662f\u56e0\u679c\u5173\u7cfb\u95ee\u9898\uff0c\u5982\u8bc4\u5206\u9884\u6d4b\u3001\u6d4f\u89c8\u540e\u7684\u70b9\u51fb\u7387\u9884\u6d4b\u3001\u70b9\u51fb\u540e\u7684\u8f6c\u6362\u7387\u9884\u6d4b\uff0c\u4ee5\u53caUplift\u5efa\u6a21\u3002\u8bb8\u591a\u5148\u524d\u7684\u7814\u7a76\u8868\u660e\uff0c\u57fa\u4e8e\u56e0\u679c\u5173\u7cfb\u7684\u63a8\u8350\u5728\u6570\u503c\u5b9e\u9a8c\u548c\u7406\u8bba\u5206\u6790\u4e2d\u90fd\u663e\u793a\u4e86\u5176\u5de8\u5927\u7684\u6f5c\u529b\u3002\u4e00\u822c\u6765\u8bf4\uff0c\u63a8\u8350\u7cfb\u7edf\u9762\u4e34\u7684\u57fa\u672c\u95ee\u9898\u662f\uff1a”\u5982\u679c\u5411\u7528\u6237\u63a8\u8350\u4e00\u4e2a\u9879\u76ee\uff0c\u4f1a\u6709\u4ec0\u4e48\u53cd\u9988”\uff0c\u9700\u8981\u4f30\u8ba1\u63a8\u8350\u5bf9\u7528\u6237\u53cd\u9988\u7684\u56e0\u679c\u6548\u5e94\u3002\u4e3a\u4e86\u56de\u7b54\u8fd9\u4e2a\u95ee\u9898\uff0c\u4eba\u4eec\u63d0\u51fa\u4e86\u8bb8\u591a\u65b9\u6cd5\uff0c\u5982\u9006\u6982\u7387\u52a0\u6743<\/span>\uff08IPS\uff09<\/span>\u3001\u81ea\u5f52\u4e00\u5316\u9006\u6982\u7387\u52a0\u6743<\/span>\uff08SNIPS\uff09<\/span>\u3001\u57fa\u4e8e\u8bef\u5dee\u63a8\u65ad<\/span>\uff08EIB\uff09<\/span>\u7684\u65b9\u6cd5\u548c\u53cc\u7a33\u5065<\/span>\uff08DR\uff09<\/span>\u65b9\u6cd5\u3002\u5728\u8fd9\u4e9b\u65b9\u6cd5\u4e2d\uff0cDR\u65b9\u6cd5\u53ca\u5176\u53d8\u4f53\u663e\u793a\u51fa\u4f18\u8d8a\u7684\u6027\u80fd\u3002<\/span><\/section>\n

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\u4e3a\u4e86\u8bc4\u4f30\u8fd9\u4e9b\u65b9\u6cd5\u7684\u6027\u80fd\uff0c\u6211\u4eec\u5f15\u5165\u4e86\u4e94\u4e2a\u89d2\u5ea6\u6765\u6bd4\u8f83\u548c\u8bc4\u4f30\u63a8\u8350\u7cfb\u7edf\u4e2d\u7684\u4e0d\u540c\u65b9\u6cd5\uff0c\u5305\u62ec\u53cc\u91cd\u7a33\u5065\u6027\u3001\u5bf9\u5c0f\u503e\u5411\u7684\u7a33\u5065\u6027\u3001\u6709\u754c\u6027\u3001\u65e0\u5916\u63a8\u6027\u548c\u4f4e\u65b9\u5dee\u3002\u5982\u679c\u4e0d\u80fd\u6ee1\u8db3\u5176\u4e2d\u4efb\u4f55\u4e00\u4e2a\uff0c\u90fd\u53ef\u80fd\u5bfc\u81f4\u6b21\u4f18\u7684\u6027\u80fd\u3002\u6211\u4eec\u5728\u8fd9\u4e94\u4e2a\u65b9\u9762\u5bf9\u5404\u79cd\u4f30\u8ba1\u5668\u8fdb\u884c\u4e86\u6bd4\u8f83\uff0c\u53d1\u73b0IPS\u3001EIB\u548cDR\u65b9\u6cd5\u53ea\u4eab\u6709\u5176\u4e2d\u51e0\u4e2a\u7406\u60f3\u7edf\u8ba1\u7279\u6027\u3002<\/span><\/section>\n

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\u3010\u63a8\u8350\u7cfb\u7edf\u4e0d\u540c\u53bb\u504f\u65b9\u6cd5\u5bf9\u6bd4\u3011<\/span><\/section>\n
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\u901a\u5e38\uff0cDR\u5728\u504f\u5dee\u548c\u65b9\u5dee\u65b9\u9762\u90fd\u4f18\u4e8eIPS\u3002\u5728\u6bd4\u8f83EIB\u548cDR\u65f6\uff0c\u6d89\u53ca\u5230\u504f\u5dee-\u65b9\u5dee\u7684\u6743\u8861\uff0c\u56e0\u4e3a\u901a\u5e38DR\u7684\u504f\u5dee\u6bd4\u8f83\u5c0f\uff0c\u800cEIB\u7684\u65b9\u5dee\u6bd4\u8f83\u5c0f\u3002\u5728\u300aDoubly Robust Collaborative Targeted Learning for Debiased Recommendations\u300b\u4e00\u6587\u4e2d\uff0c\u5317\u4eac\u5927\u5b66\u5468\u6653\u534e\u6559\u6388\u56e2\u961f\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u65b9\u6cd5\uff0c\u5373DR-TMLE\uff0c\u5b83\u53ef\u4ee5\u5229\u7528\u76ee\u6807\u6700\u5927\u4f3c\u7136\u4f30\u8ba1<\/span>\uff08TMLE\uff09<\/span>\u6280\u672f\uff0c\u6709\u6548\u5730\u6293\u4f4fDR\u548cEIB\u7684\u4f18\u70b9\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u4e0e\u4e0a\u8ff0\u73b0\u6709\u7684\u65b9\u6cd5\u76f8\u6bd4\uff0cDR-TMLE\u62e5\u6709\u66f4\u591a\u7684\u7406\u60f3\u7279\u6027\u3002\u6b64\u5916\uff0c\u4e3a\u4e86\u8fdb\u4e00\u6b65\u51cf\u5c11\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684\u504f\u5dee\u548c\u65b9\u5dee\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u9896\u7684\u4e0d\u4f9d\u8d56\u968f\u673a\u6d41\u91cf\u6570\u636e\u7684\u534f\u4f5c\u5f0f\u76ee\u6807\u5b66\u4e60\u65b9\u6cd5\uff0c\u79f0\u4e3aDR-TMLE-TL\uff0c\u5b83\u5c06\u63a8\u65ad\u8bef\u5dee\u5206\u89e3\u4e3a\u53c2\u6570\u5316\u7684\u63a8\u65ad\u6a21\u578b\u90e8\u5206\u548c\u975e\u53c2\u6570\u5316TMLE\u8bef\u5dee\u90e8\u5206\uff0c\u5176\u4e2d\u540e\u8005\u81ea\u9002\u5e94\u5730\u7ea0\u6b63\u524d\u8005\u7684\u6b8b\u5dee\u3002\u901a\u8fc7\u534f\u4f5c\u66f4\u65b0\u8fd9\u4e24\u90e8\u5206\uff0cDR-TMLE-TL\u5b9e\u73b0\u4e86\u66f4\u51c6\u786e\u548c\u7a33\u5065\u7684\u9884\u6d4b\u3002\u7406\u8bba\u5206\u6790\u548c\u5b9e\u9a8c\u90fd\u8bc1\u660e\u4e86\u4e0e\u73b0\u6709\u65b9\u6cd5\u76f8\u6bd4\uff0cDR-TMLE\u548cDR-TMLE-TL\u7684\u4f18\u8d8a\u6027\u3002<\/span><\/section>\n

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\u3010Naive\u3001EIB\u3001IPS\u3001DR\u548cDR-TMLE\u7684\u76f8\u5bf9\u8bef\u5dee\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u504f\u5dee\u3011<\/span><\/section>\n
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\u56fe\u9898\uff1aTMLE-TL\u3001TL\u548cJL\u7684MSE\uff08\u4e0a\uff09\u548cAUC\uff08\u4e0b\uff09\u7684\u6bd4\u8f83\uff0cDR\uff08\u5de6\uff09\u548cMRDR\uff08\u53f3\uff09\u4f5c\u4e3a\u8bef\u5dee\u63a8\u65ad\u6a21\u578b\uff0c\u5176\u4e2dTL\u8df3\u8fc7\u4e86TMLE-TL\u4e2d\u7684\u76ee\u6807\u6b65\u9aa4<\/span><\/p>\n


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4.\u300aRDLDR: Robust Debiasing Loop Doubly Robust Learning for Post-Click Conversion Rate Prediction\u300b\uff08\u5317\u4eac\u5927\u5b66\uff09<\/span><\/strong><\/span><\/section>\n

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\u8f6c\u5316\u6807\u7b7e\u4f5c\u4e3a\u4e00\u7c7b\u53cd\u9988\uff0c\u53cd\u6620\u4e86\u7528\u6237\u504f\u597d\u7684\u5f3a\u70c8\u4fe1\u53f7\uff0c\u5e76\u4e0e\u5546\u54c1\u4ea4\u6613\u603b\u91cf<\/span>\uff08GMV\uff09<\/span>\u76f4\u63a5\u6302\u94a9\u3002\u70b9\u51fb\u540e\u8f6c\u5316\u7387<\/span>\uff08pCVR\uff09<\/span>\u6307\u7684\u662f\u70b9\u51fb\u4e8b\u4ef6\u53d1\u751f\u540e\u7684\u8f6c\u5316\u6982\u7387\u3002\u5728\u8bb8\u591a\u5e94\u7528\u573a\u666f\u4e2d\uff0c\u5982\u7535\u5b50\u5546\u52a1\u4ea4\u6613\u3001\u97f3\u4e50\u7f51\u7ad9\u548c\u5b66\u672f\u6587\u7ae0\u4e0b\u8f7d\uff0c\u9884\u6d4bpCVR\u7684\u4efb\u52a1\u5728\u63a2\u7d22\u7528\u6237\u5174\u8da3\u548c\u589e\u52a0\u5e73\u53f0\u6536\u5165\u65b9\u9762\u8d77\u7740\u5173\u952e\u4f5c\u7528\u3002<\/span><\/section>\n

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pCVR\u9884\u6d4b\u4efb\u52a1\u53ef\u4ee5\u88ab\u770b\u4f5c\u662f\u4e00\u4e2a\u7f3a\u5931\u6570\u636e\u7684\u95ee\u9898\uff0c\u56e0\u4e3a\u6211\u4eec\u60f3\u9884\u6d4b\u6240\u6709\u4e8b\u4ef6\u7684pCVR\uff0c\u4f46\u8f6c\u6362\u53cd\u9988\u53ea\u5728\u70b9\u51fb\u7684\u4e8b\u4ef6\u4e2d\u53ef\u4ee5\u89c2\u5bdf\u5230\uff0c\u800c\u5728\u672a\u70b9\u51fb\u7684\u4e8b\u4ef6\u4e2d\u7f3a\u5931\u3002\u5728\u63a8\u8350\u7cfb\u7edf\u4e2d\uff0c\u7528\u6237\u53ef\u4ee5\u81ea\u7531\u9009\u62e9\u8981\u70b9\u51fb\u7684\u9879\u76ee\uff0c\u56e0\u6b64\u7f3a\u5931\u4e0d\u662f\u968f\u673a\u7684\uff0c\u5bfc\u81f4\u70b9\u51fb\u7684\u4e8b\u4ef6\u548c\u6240\u6709\u4e8b\u4ef6\u4e4b\u95f4\u7684\u5206\u5e03\u6709\u660e\u663e\u7684\u4e0d\u540c\uff0c\u8fd9\u5c31\u662f\u9009\u62e9\u504f\u5dee\u95ee\u9898\u3002\u4e00\u4e2a\u6734\u7d20\u7684\u65b9\u6cd5\u662f\u76f4\u63a5\u4f7f\u7528\u70b9\u51fb\u7684\u4e8b\u4ef6\u6765\u8bad\u7ec3pCVR\u6a21\u578b\u3002\u7136\u800c\uff0c\u7531\u4e8e\u9009\u62e9\u504f\u5dee\u7684\u5b58\u5728\uff0c\u8fd9\u79cd\u65b9\u6cd5\u901a\u5e38\u5177\u6709\u6b21\u6709\u7684\u6027\u80fd\u3002\u8003\u8651\u9009\u62e9\u504f\u5dee\uff0c\u65ed\u798f\u54e6\u7814\u7a76\u63d0\u51fa\u4e86\u65e0\u504f\u5b66\u4e60\u7684\u65b9\u6cd5\uff0c\u5305\u62ec\u57fa\u4e8e\u8bef\u5dee\u63a8\u65ad<\/span>\uff08EIB\uff09<\/span>\u7684\u65b9\u6cd5\u3001\u9006\u6982\u7387\u52a0\u6743<\/span>\uff08IPS\uff09<\/span>\u548c\u53cc\u7a33\u5065<\/span>\uff08DR\uff09<\/span>\u65b9\u6cd5\u3002\u5176\u4e2d\uff0c\u53cc\u7a33\u5065\u653e\u6cd5\u53ca\u5176\u63d0\u5347\u7248\u672c\u5728pCVR\u9884\u6d4b\u65b9\u9762\u5177\u6709\u6700\u5148\u8fdb\u7684\u6027\u80fd\u3002<\/span><\/section>\n

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EIB\u65b9\u6cd5\u8bd5\u56fe\u5bf9\u672a\u70b9\u51fb\u4e8b\u4ef6\u7684\u9884\u6d4b\u8bef\u5dee\u8fdb\u884c\u63a8\u65ad\uff0c\u7136\u540e\u901a\u8fc7\u5bf9\u6240\u6709\u4e8b\u4ef6\u7684\u9884\u6d4b\u8bef\u5dee\u8fdb\u884c\u5e73\u5747\uff0c\u5f97\u51fa\u4e00\u4e2a\u65e0\u504f\u7684\u635f\u5931\u51fd\u6570\u3002\u7136\u800c\uff0cEIB\u4f30\u8ba1\u5668\u5728\u5f88\u5927\u7a0b\u5ea6\u4e0a\u4f9d\u8d56\u4e8e\u8bef\u5dee\u63a8\u65ad\u6a21\u578b\u7684\u201c\u5916\u63a8\u6027<\/span>\uff08\u5916\u6cdb\u5316\u80fd\u529b\uff09<\/span>\u201d\uff0c\u5bfc\u81f4\u96be\u4ee5\u83b7\u5f97\u51c6\u786e\u7684\u8bef\u5dee\u63a8\u65ad\uff0c\u5728\u5b9e\u8df5\u4e2d\u9020\u6210\u6027\u80fd\u4e0d\u4f73\u3002IPS\u65b9\u6cd5\u5bf9\u70b9\u51fb\u7684\u6982\u7387\u8fdb\u884c\u5efa\u6a21\uff0c\u79f0\u4e3a\u503e\u5411\u5f97\u5206\uff0c\u901a\u8fc7\u7528\u503e\u5411\u5f97\u5206\u7684\u5012\u6570\u5bf9\u70b9\u51fb\u4e8b\u4ef6\u7684\u9884\u6d4b\u8bef\u5dee\u8fdb\u884c\u52a0\u6743\uff0c\u5f97\u5230\u4e00\u4e2a\u65e0\u504f\u7684\u635f\u5931\u51fd\u6570\u3002\u867d\u7136\u503e\u5411\u6027\u4f30\u8ba1\u4e0d\u4f9d\u8d56\u4e8e\u5916\u63a8\u6cd5\uff0c\u4f46IPS\u65b9\u6cd5\u9047\u5230\u4e86\u5c0f\u503e\u5411\u6027\u60c5\u51b5\u4e0b\u7684\u9ad8\u65b9\u5dee\u95ee\u9898\uff0c\u7531\u4e8e\u4e25\u91cd\u7684\u6570\u636e\u7a00\u5c11\u95ee\u9898\uff0c\u5373\u70b9\u51fb\u4e8b\u4ef6\u7684\u6837\u672c\u91cf\u8fdc\u8fdc\u5c0f\u4e8e\u672a\u70b9\u51fb\u4e8b\u4ef6\u7684\u6837\u672c\u91cf\uff0c\u8fd9\u5728pCVR\u9884\u6d4b\u4efb\u52a1\u4e2d\u7ecf\u5e38\u51fa\u73b0\u3002\u901a\u8fc7\u7ed3\u5408EIB\u548cIPS\u65b9\u6cd5\uff0cDR\u4f30\u8ba1\u5668\u5177\u6709\u53cc\u91cd\u7a33\u5065\u6027\uff0c\u4e5f\u5c31\u662f\u8bf4\uff0c\u5982\u679c\u63a8\u65ad\u7684\u8bef\u5dee\u6216\u4f30\u8ba1\u7684\u503e\u5411\u5f97\u5206\u662f\u51c6\u786e\u7684\uff0cDR\u5c31\u662f\u65e0\u504f\u7684\u3002<\/span><\/section>\n

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\u5c3d\u7ba1\u5177\u6709\u53cc\u91cd\u7a33\u5065\u6027\uff0c\u73b0\u6709\u7684DR\u65b9\u6cd5\u4e5f\u7ee7\u627f\u4e86IPS\u548cEIB\u7684\u7f3a\u9677\u3002DR\u4f30\u8ba1\u5668\u4f9d\u8d56\u4e8e\u5916\u63a8\u6cd5\uff0c\u6211\u4eec\u7684\u7406\u8bba\u5206\u6790\u8868\u660e\uff0c\u5f53\u5b58\u5728\u4e00\u4e9b\u6781\u5c0f\u7684\u503e\u5411\u6027\u65f6\uff0cDR\u4f30\u8ba1\u5668\u7684\u504f\u5dee\u3001\u65b9\u5dee\u548c\u6cdb\u5316\u8bef\u5dee\u754c\u4f1a\u53d8\u5f97\u975e\u5e38\u5927<\/span>\uff08\u8d8b\u4e8e\u65e0\u7a77\u5927\uff09<\/span>\u3002\u81ea\u5f52\u4e00\u5316\u7684IPS<\/span>\uff08SNIPS\uff09<\/span>\u4f30\u8ba1\u5668\u901a\u8fc7\u5f52\u4e00\u5316\u5176\u6743\u91cd\u6765\u6539\u8fdbIPS\u4f30\u8ba1\u5668\u3002\u4e0eDR\u4f30\u8ba1\u5668\u76f8\u6bd4\uff0c\u6211\u4eec\u53d1\u73b0SNIPS\u4f30\u8ba1\u5668\u4e0d\u4f9d\u8d56\u4e8e\u5916\u63a8\u6cd5\uff0c\u5728\u5b58\u5728\u5c0f\u7684\u503e\u5411\u6027\u7684\u60c5\u51b5\u4e0b\uff0c\u5b83\u7684\u504f\u5dee\u3001\u65b9\u5dee\u548c\u6cdb\u5316\u8bef\u5dee\u90fd\u6709\u754c\uff0c\u5c3d\u7ba1\u5b83\u4e0d\u662f\u53cc\u91cd\u7a33\u5065\u7684\u3002<\/span><\/section>\n

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\u3010\u6bd4\u8f83IPS\u3001DR\u548c\u63d0\u51fa\u7684DLDR\u4f30\u8ba1\u5668\u7684\u5916\u63a8\u4f9d\u8d56\u6027\u3001\u504f\u5dee\u3001\u65b9\u5dee\u3001\u8bef\u5dee\u754c\u548c\u5b66\u4e60\u65b9\u6cd5\u3011<\/span><\/section>\n
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\u5728\u300aRDLDR: Robust Debiasing Loop Doubly Robust Learning for Post-Click Conversion Rate Prediction\u300b\u4e00\u6587\u4e2d\uff0c\u5317\u4eac\u5927\u5b66\u5468\u6653\u534e\u6559\u6388\u56e2\u961f\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u5faa\u73af\u53bb\u504f\u53cc\u7a33\u5065<\/span>\uff08DLDR\uff09<\/span>\u4f30\u8ba1\u5668\uff0c\u5b83\u6709\u6548\u5730\u7ed3\u5408\u4e86SNIPS\u548cDR\u4f30\u8ba1\u5668\u7684\u4f18\u70b9\u3002\u4e00\u65b9\u9762\uff0c\u5f53\u5b66\u5230\u7684\u503e\u5411\u6027\u662f\u51c6\u786e\u7684\uff0cDLDR\u4f30\u8ba1\u5668\u7b49\u540c\u4e8e\u539f\u59cb\u7684SNIPS\u4f30\u8ba1\u5668\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u5982\u679c\u503e\u5411\u6027\u662f\u4e0d\u51c6\u786e\u7684\uff0c\u4f46\u63a8\u65ad\u7684\u8bef\u5dee\u662f\u51c6\u786e\u7684\uff0cDLDR\u4f30\u8ba1\u5668\u5c06\u6536\u655b\u4e8eEIB\u4f30\u8ba1\u5668\u3002\u6b64\u5916\uff0c\u6211\u4eec\u4ece\u7406\u8bba\u4e0a\u8868\u660e\uff0c\u63d0\u51fa\u7684DLDR\u4f30\u8ba1\u5668\u5728\u5b58\u5728\u5c0f\u7684\u503e\u5411\u6027\u7684\u60c5\u51b5\u4e0b\u5177\u6709\u6709\u754c\u7684\u504f\u5dee\u3001\u65b9\u5dee\u548c\u6cdb\u5316\u8bef\u5dee\u8fb9\u754c\u3002DLDR\u4f30\u8ba1\u5668\u662f\u4e00\u4e2a\u589e\u5f3a\u7684SNIPS\u4f30\u8ba1\u5668\uff0c\u5b83\u83b7\u5f97\u4e86\u53cc\u91cd\u7a33\u5065\u6027\u7684\u7279\u6027\uff0c\u5e76\u4e14\u662f\u4e00\u4e2a\u6539\u8fdb\u7684DR\u4f30\u8ba1\u5668\uff0c\u5b83\u6539\u5584\u4e86\u5bf9\u5c0f\u503e\u5411\u7684\u7a33\u5065\u6027\uff0c\u51cf\u8f7b\u4e86\u5bf9\u5916\u63a8\u6cd5\u7684\u4f9d\u8d56\u6027\u3002\u6b64\u5916\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u7a33\u5065\u7684DLDR<\/span>\uff08RDLDR\uff09<\/span>\u4e09\u6a21\u578b\u5b66\u4e60\u65b9\u6cd5\uff0c\u4ee5\u51cf\u5c11DLDR\u4f30\u8ba1\u5668\u7684\u65b9\u5dee\uff0c\u5e76\u901a\u8fc7\u5171\u4eabpCVR\u7684\u53c2\u6570\u53ca\u5176\u8bef\u5dee\u63a8\u65ad\u6a21\u578b\u8fdb\u4e00\u6b65\u63d0\u9ad8\u7a33\u5065\u6027\u3002<\/span><\/section>\n

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\u3010\u5728\u4e24\u4e2a\u534a\u5408\u6210\u6570\u636e\u96c6ML-100K\u548cML-1M\u4e0a\uff0c\u4e94\u4e2a\u9884\u6d4b\u6307\u6807\u7684\u76f8\u5bf9\u8bef\u5dee\u4e0e\u7406\u60f3\u635f\u5931\u76f8\u6bd4\u8f83\uff08\u6700\u597d\u7684\u7ed3\u679c\u7528\u9ed1\u4f53\u8868\u793a\uff09\u3011<\/span><\/section>\n
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\u3010\u5728Coat\u548cYahoo!R3\u7684MAR\u6570\u636e\u4e0a\uff0c\u4ee5MF\u4e3a\u57fa\u6a21\u578b\uff0cDCG@2,4,6\u548cRecall@2,4,6\u7684\u8bc4\u4ef7\u6307\u6807\uff08\u6700\u597d\u7684\u7ed3\u679c\u7528\u9ed1\u4f53\u8868\u793a\uff09\u3011<\/span><\/section>\n
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\u3010\u4ee5DCG@2,4,6\u548cRecall@2,4,6\u4e3a\u8bc4\u4ef7\u6307\u6807\uff0c\u5728Coat\u548cYahoo!R3\u7684MAR\u6570\u636e\u4e0a\u5bf9\u6240\u63d0\u51fa\u7684RDLDR\u7684\u65b9\u6cd5\u8fdb\u884c\u4e86\u6d88\u878d\u7814\u7a76\uff08\u6700\u597d\u7684\u7ed3\u679c\u7528\u9ed1\u4f53\u8868\u793a\uff09\u3011<\/span><\/section>\n
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5.\u300aMultiple Robust Learning for Recommendation\u300b\uff08\u5317\u4eac\u5927\u5b66\uff0c\u534e\u4e3a\u8bfa\u4e9a\u65b9\u821f\u5b9e\u9a8c\u5ba4\uff09<\/span>
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\u8fd1\u5e74\u6765\uff0c\u63a8\u8350\u7cfb\u7edf\u5728\u8bb8\u591a\u4efb\u52a1\u4e0a\u53d6\u5f97\u4e86\u663e\u8457\u7684\u8fdb\u5c55\uff0c\u5982\u5e26\u6709\u663e\u6027\u53cd\u9988\u7684\u8bc4\u5206\u9884\u6d4b\u3001\u5e26\u6709\u9690\u6027\u53cd\u9988\u7684\u7528\u6237\u4e0e\u7269\u54c1\u4e92\u52a8\u9884\u6d4b\u3001\u6d4f\u89c8\u540e\u7684\u70b9\u51fb\u7387\u9884\u6d4b\u3001\u70b9\u51fb\u540e\u7684\u8f6c\u5316\u7387\u9884\u6d4b\u4ee5\u53caUplift\u5efa\u6a21\u7b49\u3002\u5176\u4e2d\uff0c\u4e00\u4e2a\u91cd\u8981\u7684\u6311\u6218\u662f\uff0c\u7531\u4e8e\u9009\u62e9\u504f\u5dee\u548c\u6df7\u6742\u504f\u5dee\u7684\u5b58\u5728\uff0c\u6240\u6536\u96c6\u7684\u6570\u636e\u603b\u662f\u4e0d\u80fd\u4ee3\u8868\u6240\u5173\u6ce8\u7684\u6574\u4f53\u6837\u672c\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u6700\u8fd1\u4e0edebias\u76f8\u5173\u7684\u7814\u7a76\u8bbe\u8ba1\u4e86\u56e0\u679c\u5173\u7cfb\u542f\u53d1\u7684\u65b9\u6cd5\u6765\u5b9e\u73b0\u65e0\u504f\u7684\u63a8\u8350\u7cfb\u7edf\u5b66\u4e60\u3002<\/span><\/section>\n

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\u5c3d\u7ba1\u53cc\u7a33\u5065\u4f30\u8ba1\u7684\u4f18\u52bf\u5f52\u529f\u4e8e\u5b9e\u73b0\u65e0\u504f\u5b66\u4e60\u7684\u4e24\u79cd\u673a\u4f1a\uff0c\u5373\u51c6\u786e\u4f30\u8ba1\u8bef\u5dee\u63a8\u65ad\u6a21\u578b\u6216\u503e\u5411\u6a21\u578b\u7684\u673a\u4f1a\u3002\u7136\u800c\uff0c\u8fd9\u4e9b\u6a21\u578b\u7684\u6b63\u786e\u6307\u5b9a\u8981\u6c42\u5f88\u9ad8\u3002\u5373\u4f7f\u6240\u5b66\u7684\u4e24\u4e2a\u6a21\u578b\u90fd\u6709\u8f7b\u5fae\u7684\u6307\u5b9a\u9519\u8bef\uff0c\u90a3\u4e48\u6b63\u5982\u73b0\u6709\u7684\u5de5\u4f5c\u6240\u6307\u51fa\u7684\uff0c\u53cc\u7a33\u5065\u5b66\u4e60\u7684\u504f\u5dee\u4f1a\u5f88\u4e25\u91cd\u3002\u66f4\u7cdf\u7cd5\u7684\u662f\uff0c\u5373\u4f7f\u6709\u6b63\u786e\u7684\u6a21\u578b\u6307\u5b9a\uff0c\u8981\u6839\u636e\u89c2\u5bdf\u5230\u7684\u6570\u636e\u51c6\u786e\u5730\u5b66\u4e60\u5b83\u4eec\u4e5f\u662f\u6709\u96be\u5ea6\u7684\u3002\u9996\u5148\uff0c\u51c6\u786e\u7684\u9884\u6d4b\u8bef\u5dee\u7684\u4f30\u8ba1\u5f88\u96be\u83b7\u5f97\uff0c\u56e0\u4e3a\u8bef\u5dee\u63a8\u65ad\u6a21\u578b\u662f\u5728\u66b4\u9732\u7684\u4e8b\u4ef6\u4e2d\u5b66\u4e60\u7684\uff0c\u800c\u88ab\u7528\u4e8e\u672a\u66b4\u9732\u7684\u4e8b\u4ef6\u3002\u5982\u679c\u66b4\u9732\u7684\u4e8b\u4ef6\u548c\u672a\u66b4\u9732\u7684\u4e8b\u4ef6\u7684\u5206\u5e03\u6709\u5f88\u5927\u7684\u4e0d\u540c\uff0c\u8fd9\u5f88\u5bb9\u6613\u9020\u6210\u5f88\u5927\u7684\u504f\u5dee\u3002\u5176\u6b21\uff0c\u6536\u96c6\u5230\u7684\u6570\u636e\u603b\u662f\u5305\u542b\u8bb8\u591a\u504f\u5dee<\/span>\uff08\u4f8b\u5982\uff0c\u7531\u672a\u89c2\u5bdf\u5230\u7684\u6df7\u6742\u56e0\u7d20\u5f15\u8d77\u7684\u504f\u5dee\uff09<\/span>\uff0c\u8fd9\u53ef\u80fd\u5bfc\u81f4\u5bf9\u503e\u5411\u6027\u7684\u4e0d\u51c6\u786e\u4f30\u8ba1\u3002\u56e0\u6b64\uff0c\u9700\u8981\u5bf9\u65e0\u504f\u7684\u5b66\u4e60\u8fdb\u884c\u66f4\u591a\u7684\u7814\u7a76\u5de5\u4f5c\u3002<\/span><\/section>\n

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\u5728\u300aMultiple Robust Learning for Recommendation\u300b\u4e00\u6587\u4e2d\uff0c\u5317\u4eac\u5927\u5b66\u5468\u6653\u534e\u6559\u6388\u56e2\u961f\u548c\u534e\u4e3a\u8bfa\u4e9a\u65b9\u821f\u5b9e\u9a8c\u5ba4\u63d0\u51fa\u4e86\u4e00\u4e2a\u7528\u4e8e\u63a8\u8350\u7cfb\u7edf\u4e2d\u65e0\u504f\u5b66\u4e60\u7684\u591a\u91cd\u7a33\u5065\uff08MR\uff09\u4f30\u8ba1\u5668\uff0c\u5b83\u5305\u542b\u591a\u4e2a\u5019\u9009\u503e\u5411\u6a21\u578b\u548c\u8bef\u5dee\u63a8\u65ad\u6a21\u578b\u3002\u5b83\u5141\u8bb8\u591a\u79cd\u4e0d\u540c\u89c4\u683c\u7684\u503e\u5411\u548c\u8bef\u5dee\u63a8\u65ad\u6a21\u578b\u7684\u5b66\u4e60\u3002\u901a\u8fc7\u7406\u8bba\u5206\u6790\uff0c\u6211\u4eec\u8bc1\u660e\u4e86\u6240\u63d0\u51fa\u7684MR\u4f30\u8ba1\u5668\u5177\u6709\u591a\u91cd\u7a33\u5065\u6027\uff0c\u5982\u679c\u4efb\u4f55\u4e00\u4e2a\u503e\u5411\u6a21\u578b\u3001\u8bef\u5dee\u63a8\u65ad\u6a21\u578b\uff0c\u751a\u81f3\u8fd9\u4e9b\u6a21\u578b\u7684\u7ebf\u6027\u7ec4\u5408\u90fd\u80fd\u51c6\u786e\u4f30\u8ba1\u51fa\u771f\u5b9e\u7684\u503e\u5411\u6216\u9884\u6d4b\u8bef\u5dee\uff0c\u90a3\u4e48\u5b83\u5c31\u662f\u65e0\u504f\u7684\u3002\u56e0\u6b64\uff0cMR\u4f30\u8ba1\u5668\u901a\u8fc7\u63d0\u4f9b\u66f4\u591a\u7684\u6a21\u578b\u89c4\u8303\u548c\u5b66\u4e60\u673a\u4f1a\uff0c\u53ef\u4ee5\u5927\u5927\u89e3\u51b3\u73b0\u6709\u5de5\u4f5c\u4e2d\u5355\u4e00\u6a21\u578b\u6240\u5b66\u7684\u503e\u5411\u6027\u6216\u63a8\u65ad\u8bef\u5dee\u4e0d\u51c6\u786e\u7684\u95ee\u9898\u3002\u6b64\u5916\uff0c\u6211\u4eec\u7279\u522b\u5206\u6790\u4e86\u5f53MR\u53ea\u6709\u4e00\u4e2a\u503e\u5411\u6027\u6a21\u578b\u548c\u4e00\u4e2a\u5f52\u56e0\u6a21\u578b\u65f6\uff0cMR\u548cDR\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u53d1\u73b0\u6211\u4eec\u7684MR\u4f30\u8ba1\u5668\u5b9e\u9645\u4e0a\u53ef\u4ee5\u63d0\u4f9b\u4e00\u4e2a\u52a0\u5f3a\u7248\u7684\u53cc\u91cd\u7a33\u5065\u6027\u3002<\/span><\/section>\n

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\u901a\u8fc7\u7406\u8bba\u5206\u6790MR\u4f30\u8ba1\u5668\u7684\u6cdb\u5316\u8bef\u5dee\u754c\uff0c\u6211\u4eec\u8fdb\u4e00\u6b65\u63d0\u51fa\u4e86\u4e00\u79cd\u5177\u6709\u7a33\u5b9a\u6027\u7684\u591a\u91cd\u9c81\u68d2\u6027\u5b66\u4e60\u65b9\u6cd5\uff0c\u901a\u8fc7\u6dfb\u52a0\u6b63\u5219\u5316\u66f4\u597d\u5730\u63a7\u5236\u6cdb\u5316\u8bef\u5dee\u3002\u5728\u771f\u5b9e\u4e16\u754c\u548c\u534a\u5408\u6210\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u7684\u5927\u91cf\u5b9e\u9a8c\u8868\u660e\uff0c\u4e0e\u6700\u5148\u8fdb\u7684\u65b9\u6cd5\u76f8\u6bd4\uff0c\u6240\u63d0\u51fa\u7684\u65b9\u6cd5\u53d6\u5f97\u4e86\u660e\u663e\u7684\u6539\u8fdb\u3002<\/span><\/section>\n

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\u3010\u4ee5MF\u548cNCF\u4e3a\u9aa8\u5e72\u6a21\u578b\u5bf9Coat\u548cYahoo\u8fdb\u884c\u7684\u5b9e\u9a8c\u7ed3\u679c\u3011<\/span><\/section>\n
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\u3010\u5728\u4e0d\u540c\u7684\u8bef\u5dee\u63a8\u65ad\u6a21\u578b\u8bbe\u7f6e\u4e0b\uff0c\u5373\u4e0d\u540c\u7684\u6570\u91cf\u548c\u7c7b\u578b\uff0cMR\u65b9\u6cd5\u5bf9Coat\u7684\u6027\u80fd\u3011<\/span><\/section>\n
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\u3010\u5728Coat\u6570\u636e\u96c6\u4e0a\u4e0d\u540c\u6570\u91cf\u548c\u7c7b\u578b\u7684\u503e\u5411\u6027\u6a21\u578b\u4e0b\uff0cMR\u65b9\u6cd5\u7684\u6027\u80fd\uff0c\u5176\u4e2d\u8bef\u5dee\u63a8\u65ad\u6a21\u578b\u548c\u9aa8\u5e72\u9884\u6d4b\u6a21\u578b\u90fd\u91c7\u7528MF\u3011<\/span><\/section>\n
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6.\u300aA Generic Unbiased Learning Framework from Explicit to Implicit Feedback with Unmeasured Confounders\u300b(\u5317\u4eac\u5927\u5b66)<\/span><\/strong><\/span>
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\u63a8\u8350\u7cfb\u7edf\u5728\u8bb8\u591a\u73b0\u4ee3\u7f51\u7ad9\u548c\u79fb\u52a8\u5e94\u7528\u4e2d\u53d1\u6325\u7740\u5173\u952e\u4f5c\u7528\uff0c\u5b83\u8fc7\u6ee4\u51fa\u7528\u6237\u53ef\u80fd\u611f\u5174\u8da3\u7684\u4fe1\u606f\u3002\u4e00\u822c\u6765\u8bf4\uff0c\u6709\u4e24\u79cd\u7c7b\u578b\u7684\u7528\u6237\u53cd\u9988\uff0c\u5373\u663e\u6027\u53cd\u9988\u548c\u9690\u6027\u53cd\u9988\uff0c\u7528\u4e8e\u8bad\u7ec3\u63a8\u8350\u6a21\u578b\u3002\u663e\u6027\u53cd\u9988<\/span>\uff08\u5982\u8bc4\u5206\uff09<\/span>\u6beb\u4e0d\u542b\u7cca\u5730\u53cd\u6620\u4e86\u7528\u6237\u7684\u504f\u597d\u3002\u7136\u800c\uff0c\u6536\u96c6\u663e\u6027\u53cd\u9988\u9700\u8981\u989d\u5916\u7684\u5de5\u4f5c\uff0c\u56e0\u6b64\u5f80\u5f80\u662f\u4e0d\u53ef\u83b7\u53d6\u7684\u3002\u76f8\u6bd4\u4e4b\u4e0b\uff0c\u9690\u6027\u53cd\u9988<\/span>\uff08\u5982\u70b9\u51fb\u6216\u8d2d\u4e70\uff09<\/span>\u53ea\u63ed\u793a\u4e86\u7528\u6237\u7684\u90e8\u5206\u504f\u597d\uff0c\u4f46\u53ea\u9700\u8bb0\u5f55\u7528\u6237\u7684\u884c\u4e3a\u65e5\u5fd7\u5c31\u53ef\u4ee5\u8f7b\u677e\u83b7\u5f97\u3002<\/span><\/section>\n

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\u7531\u4e8e\u7f3a\u4e4f\u660e\u786e\u7684\u8d1f\u9762\u53cd\u9988\u548c\u7f3a\u5931\u975e\u968f\u673a<\/span>\uff08MNAR\uff09<\/span>\u7684\u95ee\u9898\uff0c\u5229\u7528\u9690\u6027\u53cd\u9988\u5efa\u7acb\u63a8\u8350\u6a21\u578b\u662f\u5177\u6709\u6311\u6218\u6027\u7684\u3002\u4f8b\u5982\uff0c\u5728\u4fe1\u606f\u68c0\u7d22\u9886\u57df\uff0c\u5047\u8bbe\u70b9\u51fb\u8868\u793a\u6b63\u53cd\u9988\uff0c\u90a3\u4e48\u4eba\u4eec\u5c31\u65e0\u6cd5\u77e5\u9053\u672a\u70b9\u51fb\u7684\u4e8b\u4ef6\u662f\u8d1f\u53cd\u9988<\/span>\uff08\u4e0d\u76f8\u5173\uff09<\/span>\u8fd8\u662f\u672a\u66b4\u9732\u7684\u53cd\u9988\uff0c\u4e5f\u5c31\u662f\u8bf4\uff0c\u771f\u6b63\u7684\u8d1f\u53cd\u9988\u548c\u6f5c\u5728\u7684\u6b63\u53cd\u9988\u5728\u672a\u70b9\u51fb\u7684\u4e8b\u4ef6\u4e2d\u7ea0\u7f20\u5728\u4e00\u8d77\u3002\u8fd9\u4e2a\u95ee\u9898\u4e5f\u88ab\u79f0\u4e3a\u6b63\u5411\u65e0\u6807\u8bb0\u7684\u95ee\u9898\u3002\u6b64\u5916\uff0c\u7531\u4e8e\u7528\u6237\u503e\u5411\u4e8e\u70b9\u51fb\u66f4\u76f8\u5173\u7684\u9879\u76ee\uff0c\u800c\u7cfb\u7edf\u66f4\u503e\u5411\u4e8e\u63a8\u8350\u70ed\u95e8\u9879\u76ee\uff0c\u6240\u4ee5\u7528\u6237\u7684\u53cd\u9988\u5e76\u4e0d\u662f\u5b8c\u5168\u968f\u673a\u7f3a\u5931\u7684\u3002\u5ffd\u89c6\u8fd9\u4e2a\u95ee\u9898\u4f1a\u62db\u81f4\u504f\u89c1\uff0c\u5bfc\u81f4\u6b21\u4f18\u6027\u80fd\u3002<\/span><\/section>\n

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\u5927\u91cf\u7684\u6587\u732e\u4e13\u6ce8\u4e8e\u89e3\u51b3\u6b63\u5411\u65e0\u6807\u7b7e\u95ee\u9898\uff0c\u4f8b\u5982\u5c06\u672a\u70b9\u51fb\u7684\u4e8b\u4ef6\u4f5c\u4e3a\u8d1f\u5411\u6837\u672c\uff0c\u5e76\u7ed9\u5b83\u4eec\u5206\u914d\u8f83\u5c11\u7684\u6743\u91cd\uff0c\u6216\u8005\u8bd5\u56fe\u4ece\u672a\u70b9\u51fb\u7684\u4e8b\u4ef6\u4e2d\u62bd\u53d6\u4e00\u4e9b\u6709\u4ee3\u8868\u6027\u7684\u9879\u76ee\u3002\u8fd1\u5e74\u6765\uff0c\u4eba\u4eec\u5bf9\u540c\u65f6\u89e3\u51b3\u6b63\u9762\u65e0\u6807\u7b7e\u95ee\u9898\u548cMNAR\u95ee\u9898\u7684\u5174\u8da3\u8d8a\u6765\u8d8a\u5927\u3002Saito\u7b49\u4eba\u5236\u5b9a\u4e86\u4e00\u4e2a\u65b0\u7684\u7406\u60f3\u635f\u5931\u51fd\u6570\uff0c\u7136\u540e\u63d0\u51fa\u4e86\u4e00\u4e2a\u7528\u4e8e\u65e0\u504f\u5b66\u4e60\u7684\u9006\u6982\u7387\u52a0\u6743<\/span>\uff08IPW\uff09<\/span>\u65b9\u6cd5\u3002Zhu\u7b49\u4eba\u548cLee\u7b49\u4eba\u5206\u522b\u901a\u8fc7\u5f15\u5165\u8054\u5408\u5b66\u4e60\u65b9\u6cd5\u548c\u5904\u7406\u7f3a\u5931\u53cd\u9988\u7684\u504f\u5dee\u6765\u6269\u5c55Saito\u7684\u65b9\u6cd5\u3002\u6b64\u5916\uff0cLee\u7b49\u4eba\u8fdb\u4e00\u6b65\u6307\u51fa\u4e86\u66b4\u9732\u504f\u5dee\u7684\u5b58\u5728\uff0c\u5e76\u63d0\u51fa\u4e86\u4e00\u79cd\u53bb\u504f\u65b9\u6cd5\u6765\u5904\u7406\u5b83\u3002<\/span><\/section>\n

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\u5c3d\u7ba1\u73b0\u6709\u7684\u9690\u6027\u53cd\u9988\u7684\u53bb\u504f\u79fb\u65b9\u6cd5\u6bd4\u5ffd\u7565MNAR\u95ee\u9898\u7684\u65b9\u6cd5\u63d0\u9ad8\u4e86\u9884\u6d4b\u7cbe\u5ea6\uff0c\u5728\u300aA Generic Unbiased Learning Framework from Explicit to Implicit Feedback with Unmeasured Confounders\u300b\u4e00\u6587\u4e2d\uff0c\u5317\u4eac\u5927\u5b66\u5468\u6653\u534e\u6559\u6388\u56e2\u961f\u53d1\u73b0\u6709\u4e24\u4e2a\u91cd\u8981\u7684\u9650\u5236\uff1a\uff081\uff09\u6ca1\u6709\u53cc\u7a33\u5065<\/span>\uff08DR\uff09<\/span>\u65b9\u6cd5\u3002\u4f17\u6240\u5468\u77e5\uff0c\u53cc\u7a33\u5065\u65b9\u6cd5\u53ca\u5176\u53d8\u4f53\u5728\u663e\u6027\u53cd\u9988\u7684\u65e0\u504f\u5b66\u4e60\u4e2d\u53d6\u5f97\u4e86\u6700\u5148\u8fdb\u7684\u6027\u80fd\uff0c\u800c\u4e14\u8bb8\u591a\u65b0\u7684\u7b97\u6cd5\u548c\u6a21\u578b\u7ed3\u6784\u88ab\u8bbe\u8ba1\u7528\u6765\u63d0\u9ad8DR\u65b9\u6cd5\u7684\u6027\u80fd\u3002\u56e0\u6b64\uff0c\u5f00\u53d1\u9690\u6027\u53cd\u9988\u7684DR\u65b9\u6cd5\u662f\u5fc5\u8981\u7684\uff1b(2)\u6ca1\u6709\u4e00\u4e2a\u65b9\u6cd5\u53ef\u4ee5\u89e3\u51b3\u672a\u6d4b\u91cf\u7684\u6df7\u6742\u56e0\u7d20\u3002\u7531\u4e8e\u5404\u79cd\u539f\u56e0\uff0c\u5982\u6280\u672f\u56f0\u96be\u6216\u9690\u79c1\u9650\u5236\uff0c\u672a\u6d4b\u91cf\u7684\u6df7\u6742\u56e0\u7d20\u662f\u63a8\u8350\u7cfb\u7edf\u4e2d\u4e0d\u53ef\u907f\u514d\u7684\u95ee\u9898\u3002\u6211\u4eec\u7684\u7406\u8bba\u5206\u6790\u8868\u660e\uff0c\u5f53\u672a\u6d4b\u91cf\u7684\u6df7\u6742\u56e0\u7d20\u5b58\u5728\u65f6\uff0c\u57fa\u4e8e\u503e\u5411\u5f97\u5206\u7684\u53bb\u504f\u65b9\u6cd5\u662f\u6709\u504f\u5dee\u7684\uff0c\u56e0\u6b64\u5728\u63a8\u8350\u7cfb\u7edf\u4e2d\u5f00\u53d1\u4e00\u79cd\u5bf9\u6297\u672a\u6d4b\u91cf\u7684\u6df7\u6742\u56e0\u7d20\u7684\u65b9\u6cd5\u81f3\u5173\u91cd\u8981\u3002<\/span><\/section>\n

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\u56fe\u9898\uff1a\u5b58\u5728\u672a\u89c2\u6d4b\u6df7\u6742H\u65f6\u7684\u9690\u53cd\u9988\u5178\u578b\u56e0\u679c\u56fe<\/span><\/section>\n

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\u4e3a\u4e86\u586b\u8865\u8fd9\u4e9b\u7814\u7a76\u7a7a\u767d\uff0c\u6211\u4eec\u9996\u5148\u521b\u65b0\u6027\u5730\u5c06\u9690\u6027\u53cd\u9988\u95ee\u9898\u8868\u8ff0\u4e3a\u6f5c\u5728\u7ed3\u679c\u6846\u67b6\u5185\u7684\u53cd\u4e8b\u5b9e\u9884\u6d4b\u95ee\u9898\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u5728\u8fd9\u79cd\u65b0\u7684\u95ee\u9898\u8868\u8ff0\u4e0b\uff0c\u4ee5\u524d\u6240\u6709\u9488\u5bf9\u663e\u6027\u53cd\u9988\u7684\u53bb\u504f\u65b9\u6cd5\u90fd\u53ef\u4ee5\u5e73\u884c\u5730\u5e94\u7528\u4e8e\u9690\u6027\u53cd\u9988\uff0c\u524d\u63d0\u662f\u66b4\u9732\u72b6\u6001<\/span>\uff08\u6216\u7b49\u540c\u4e8e\u6cbb\u7597\uff09<\/span>\u53ef\u4ee5\u88ab\u9002\u5f53\u5730\u63a8\u65ad\u3002\u8fd9\u4fc3\u4f7f\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u4e24\u9636\u6bb5\u7684\u53bb\u504f\u6846\u67b6\uff0c\u5176\u4e2d\u5305\u62ec\u6cbb\u7597\u7684\u63a8\u65ad\u9636\u6bb5\u548c\u53bb\u504f\u9636\u6bb5\u3002\u6cbb\u7597\u7684\u5f52\u7eb3\u662f\u4e00\u4e2a\u5355\u7c7b\u7684\u5206\u7c7b\u95ee\u9898\uff0c\u6211\u4eec\u5229\u7528\u5728\u8fd9\u4e00\u9886\u57df\u88ab\u5e7f\u6cdb\u4f7f\u7528\u7684SVDD\u65b9\u6cd5\u3002\u5bf9\u4e8e\u53bb\u504f\u9636\u6bb5\uff0c\u9664\u4e86\u76f4\u63a5\u5e94\u7528\u4ee5\u524d\u7528\u4e8e\u663e\u6027\u53cd\u9988\u7684\u53bb\u504f\u65b9\u6cd5\u5916\uff0c\u6211\u4eec\u8fd8\u8003\u8651\u4e86\u672a\u89c2\u6d4b\u6df7\u6742\u7684\u5b58\u5728\uff0c\u8fd9\u5728\u9690\u6027\u53cd\u9988\u4e2d\u8fd8\u6ca1\u6709\u7814\u7a76\u8fc7\u3002\u91cd\u8981\u7684\u662f\uff0c\u6240\u63d0\u51fa\u7684\u6846\u67b6\u63d0\u4f9b\u4e86\u4e00\u4e2a\u7075\u6d3b\u7684\u8303\u5f0f\uff0c\u5c06\u663e\u6027\u53cd\u9988\u4e2d\u7684\u53bb\u504f\u65b9\u6cd5\u4e0e\u9690\u6027\u53cd\u9988\u4e2d\u7684\u53bb\u504f\u65b9\u6cd5\u65e0\u7f1d\u8fde\u63a5\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u663e\u6027\u53cd\u9988\u4e2d\u7684DR\u53bb\u504f\u65b9\u6cd5\u5e94\u7528\u4e8e\u9690\u6027\u53cd\u9988\u3002<\/span><\/section>\n

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\u56fe\u9898\uff1a\u5728MAR\u6570\u636e\u4e0a\uff0c\u4e0d\u540c\u5bf9\u6297\u5f3a\u5ea6\u7684AUC\u3001NDCG@5\u548cNDCG@10\u3002\u4e0a\u9762\u662fIPS\u548cDR\u65b9\u6cd5\uff0c\u4e0b\u9762\u662f\u57fa\u4e8eDR\u548cMRDR\u7684\u8054\u5408\u5b66\u4e60\u65b9\u6cd5\u3002<\/span><\/p>\n


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\u53c2\u8003\u6587\u732e\uff1a<\/span><\/strong><\/span><\/section>\n

[1] Peng Wu, Haoxuan Li, Yuhao Deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, Xiao-Hua Zhou (2021), On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges, IJCAI 22.<\/span><\/p>\n

[2] Quanyu Dai, Haoxuan Li, Peng Wu, Zhenhua Dong, Xiao-Hua Zhou, Rui Zhang, Xiuqiang He, Rui Zhang, and Jie Sun (2022), A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction, KDD 22.<\/span><\/p>\n

[3] Peng Wu, Haoxuan Li, Yan Lyu, Chunyuan Zheng & Xiao-Hua Zhou (2022), Doubly Robust Collaborative Targeted Learning for Recommendation on Data Missing Not at Random, arXiv:2203.10258.<\/span><\/p>\n

[4] Haoxuan Li, Chunyuan Zheng, Peng Wu & Xiao-Hua Zhou (2022), RDLDR: Robust Debiasing Loop Doubly Robust Learning for Post-Click Conversion Rate Prediction.<\/span><\/p>\n

[5] Haoxuan Li, Quanyu Dai, Yuru Li, Yan Lyu, Zhenhua Dong, Peng Wu & Xiao-Hua Zhou (2022), Multiple Robust Learning for Recommendation.<\/span><\/p>\n

[6] Yan Lyu, Haoxuan Li, Chunyuan Zheng, Peng Wu & Xiao-Hua Zhou (2022), A Generic Unbiased Learning Framework from Explicit to Implicit Feedback with Unmeasured Confounders.<\/span><\/p>\n


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2022\u7b2c\u56db\u5c4a\u6cdb\u592a\u5e73\u6d0b\u56e0\u679c\u63a8\u65ad\u5927\u4f1a<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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\u56e0\u679c\u63a8\u7406\u7684\u76ee\u6807\u662f\u5c06\u5916\u90e8\u77e5\u8bc6\u4e0e\u5b66\u4e60\u8bbe\u8ba1\u7ed3\u5408\u8d77\u6765\uff0c\u5f97\u5230\u53d8\u91cf\u4e4b\u95f4\u7684\u56e0\u679c\u5173\u7cfb\u3002\u5c24\u5176\u662f\u5728\u4eba\u5de5\u667a\u80fd\u6280\u672f\u906d\u9047\u74f6\u9888\u7684\u73b0\u5728\uff0c\u56e0\u679c\u63a8\u7406\u7684\u65b9\u6cd5\u4e5f\u8d8a\u6765\u8d8a\u53d7\u5230\u4e0d\u540c\u9886\u57df\u7684\u91cd\u89c6\uff0c\u5b83\u5728\u4eba\u5de5\u667a\u80fd\u3001\u7edf\u8ba1\u5b66\u3001\u751f\u7269\u7edf\u8ba1\u5b66\u3001\u751f\u7269\u533b\u5b66\u3001\u8ba1\u7b97\u673a\u79d1\u5b66\u3001\u7ecf\u6d4e\u5b66\u3001\u6d41\u884c\u75c5\u5b66\u548c\u5404\u79cd\u793e\u4f1a\u79d1\u5b66\u7b49\u9886\u57df\u83b7\u5f97\u4e86\u5e7f\u6cdb\u7684\u5e94\u7528\u3002<\/span>
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\u4e3a\u4e86\u66f4\u591a\u5730\u63a8\u52a8\u56e0\u679c\u79d1\u5b66\u5b66\u79d1\u7684\u53d1\u5c55\uff0c\u805a\u96c6\u56fd\u5185\u5916\u56e0\u679c\u79d1\u5b66\u7684\u4e00\u7ebf\u79d1\u7814\u5de5\u4f5c\u8005\uff0c\u5171\u540c\u8ba8\u8bba\u56e0\u679c\u79d1\u5b66\u7684\u6700\u65b0\u8fdb\u5c55\uff0c\u5317\u4eac\u5927\u5b66\u8bb2\u5e2d\u6559\u6388\u3001\u5317\u4eac\u5927\u5b66\u516c\u5171\u536b\u751f\u5b66\u9662\u751f\u7269\u7edf\u8ba1\u7cfb\u7cfb\u4e3b\u4efb\u3001\u5317\u4eac\u5927\u5b66\u5317\u4eac\u56fd\u9645\u6570\u5b66\u7814\u7a76\u4e2d\u5fc3\u751f\u7269\u7edf\u8ba1\u548c\u4fe1\u606f\u7814\u7a76\u5ba4\u4e3b\u4efb\u5468\u6653\u534e\u7b49\u53d1\u8d77\u4e86\u6cdb\u592a\u5e73\u6d0b\u56e0\u679c\u63a8\u65ad\u5927\u4f1a\uff0c\u672c\u6b21\u4f1a\u8bae\u5c06\u96c6\u4e2d\u8ba8\u8bba\u56e0\u679c\u79d1\u5b66\u5728\u4e0d\u540c\u9886\u57df\u7684\u6700\u65b0\u8fdb\u5c55\u3002<\/span><\/section>\n

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\u8be5\u4f1a\u8bae\u66fe\u7ecf\u9080\u8bf7\u8fc7Donald B. Rubin, James M. Robins, Bernhard Sch\u00f6lkopf, Eric J. Tchetgen, Xiao-Hua Zhou, Peng Cui, Kun Zhang, Peng Ding\u7b49\u6d77\u5185\u5916\u7684\u5b66\u672f\u5927\u62ff\u5171\u540c\u5206\u4eab\u6700\u65b0\u7684\u79d1\u7814\u8fdb\u5c55\uff0c\u5e76\u8fdb\u884c\u6df1\u5ea6\u63a2\u8ba8\u3002\u7b2c\u4e8c\u5c4a\u5927\u4f1a\uff082020\u5e74\uff09\u4e0e\u7b2c\u4e09\u5c4a\u5927\u4f1a\uff082021\u5e74\uff09\u5185\u5bb9\u56de\u987e\u53c2\u89c1\uff1a<\/span><\/section>\n

2020\u5e74\u6cdb\u592a\u5e73\u6d0b\u56e0\u679c\u63a8\u65ad\u5927\u4f1a\u56de\u987e\uff08\u4e00\uff09<\/strong><\/span><\/a><\/p>\n

2020\u5e74\u6cdb\u592a\u5e73\u6d0b\u56e0\u679c\u63a8\u65ad\u5927\u4f1a\u56de\u987e\uff08\u4e8c\uff09<\/strong><\/span><\/a><\/p>\n

2021\u5e74\u6cdb\u592a\u5e73\u6d0b\u56e0\u679c\u63a8\u65ad\u5927\u4f1a\u56de\u987e<\/strong><\/span><\/a><\/p>\n


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\u5177\u4f53\u8bf7\u79fb\u6b65\u6cdb\u592a\u5e73\u6d0b\u56e0\u679c\u63a8\u65ad\u5927\u4f1a\u5b98\u65b9\u7f51\u7ad9\u67e5\u770b\u8be6\u60c5\uff1a<\/span><\/p>\n

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\u56e0\u679c\u63a8\u7406\u5728\u7edf\u8ba1\u5b66\u3001\u751f\u7269\u7edf\u8ba1\u5b66\u3001\u751f\u7269\u533b\u5b66\u3001\u8ba1\u7b97\u673a\u79d1\u5b66\u3001\u7ecf\u6d4e\u5b66\u3001\u6d41\u884c\u75c5\u5b66\u548c\u793e\u4f1a\u79d1\u5b66\u7b49\u9886\u57df\u90fd\u5f97\u5230\u4e86\u5e7f\u6cdb\u7684\u5e94\u7528\u30022022\u5e74\u5ea6\u7b2c\u56db\u5c4a\u6cdb\u592a\u5e73\u6d0b\u56e0\u679c\u63a8\u65ad\u5927\u4f1a\uff082022 Pacific Causal Inference Conference\uff09\u5c06\u4e8e9\u670817\u65e5\u81f39\u670818\u65e5\u7ebf\u4e0a\u4e3e\u884c\u3002\u672c\u6b21\u4f1a\u8bae\u5c06\u96c6\u4e2d\u8ba8\u8bba\u56e0\u679c\u63a8\u7406\u7684\u6700\u65b0\u8fdb\u5c55\uff0c\u8bda\u9080\u56fd\u5185\u5916\u76f8\u5173\u4e13\u5bb6\u5b66\u8005\u62e8\u5197\u53c2\u52a0\u3002<\/span><\/section>\n

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\u62a5\u540d\u65b9\u5f0f\uff1a<\/span><\/strong>
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1. \u626b\u7801\u586b\u5199\u62a5\u540d\u4fe1\u606f\uff0c\u5e76\u52a0\u5165\u5fae\u4fe1\u7fa4\uff08\u62a5\u540d\u622a\u6b62\u65e5\u671f\uff1a2022\u5e749\u670818\u65e5\uff09\u3002<\/span><\/section>\n

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\u626b\u7801\u6ce8\u518c\u8fdb\u7fa4<\/span><\/section>\n

2.\u4f1a\u8bae\u5c06\u7ebf\u4e0a\u8fdb\u884c\uff0c\u5177\u4f53\u4fe1\u606f\u5c06\u4f1a\u5728\u5fae\u4fe1\u7fa4\u5185\u901a\u77e5\u3002<\/span><\/p>\n

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\u76f8\u5173\u8be6\u60c5\uff1a<\/span><\/section>\n
2022\u7b2c\u56db\u5c4a\u6cdb\u592a\u5e73\u6d0b\u56e0\u679c\u63a8\u65ad\u5927\u4f1a9\u670817\u65e5-18\u65e5\u5728\u7ebf\u4e3e\u529e\uff0c\u62a5\u540d\u8fdb\u884c\u65f6<\/a><\/span><\/strong><\/span><\/section>\n
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\u63a8\u8350\u9605\u8bfb<\/span><\/strong><\/strong><\/p>\n