{"id":92552,"date":"2026-08-10T15:16:52","date_gmt":"2026-08-10T07:16:52","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/92552.html"},"modified":"2026-08-10T15:16:52","modified_gmt":"2026-08-10T07:16:52","slug":"instagram%e6%8e%a8%e8%8d%90%e7%ae%97%e6%b3%95%e6%8a%80%e6%9c%af%e5%85%a8%e8%a7%a3%e6%9e%90%ef%bc%9a%e4%bb%8e%e5%8d%8f%e5%90%8c%e8%bf%87%e6%bb%a4%e5%88%b0%e7%94%9f%e6%88%90%e5%bc%8f%e6%8e%a8%e8%8d%90","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/92552.html","title":{"rendered":"instagram\u63a8\u8350\u7b97\u6cd5\u6280\u672f\u5168\u89e3\u6790\uff1a\u4ece\u534f\u540c\u8fc7\u6ee4\u5230\u751f\u6210\u5f0f\u63a8\u8350\u7684\u5b8c\u6574\u94fe\u8def\u62c6\u89e3"},"content":{"rendered":"

\u63a8\u8350\u7b97\u6cd5\u7684\u672c\u8d28\u662f\u5174\u8da3\u5339\u914d\u7684\u6982\u7387\u8ba1\u7b97<\/p>\n

\u5f88\u591a\u7528\u6237\u5728\u4f7f\u7528Instagram\u65f6\u90fd\u4f1a\u4ea7\u751f\u7591\u95ee：\u4e3a\u4ec0\u4e48\u5e73\u53f0\u63a8\u8350\u7684\u5185\u5bb9\u8d8a\u6765\u8d8a\u7b26\u5408\u81ea\u5df1\u7684\u504f\u597d？\u4e8b\u5b9e\u4e0a，\u63a8\u8350\u7cfb\u7edf\u5e76\u4e0d\u5177\u5907\u8bfb\u61c2\u7528\u6237\u60f3\u6cd5\u7684\u80fd\u529b，\u5176\u672c\u8d28\u662f\u901a\u8fc7\u5206\u6790\u7528\u6237\u884c\u4e3a\u6570\u636e\u3001\u5185\u5bb9\u7279\u5f81，\u8ba1\u7b97\u7528\u6237\u4e0e\u5185\u5bb9\u7684\u5339\u914d\u6982\u7387，\u6700\u7ec8\u7b5b\u9009\u51fa\u7528\u6237\u53ef\u80fd\u611f\u5174\u8da3\u7684\u5185\u5bb9\u3002<\/p>\n

\u65e9\u671f\u4e92\u8054\u7f51\u5e73\u53f0\u7684\u5185\u5bb9\u5c55\u793a\u591a\u91c7\u7528\u65f6\u95f4\u5e8f\u6392\u5e8f，\u7528\u6237\u83b7\u53d6\u611f\u5174\u8da3\u7684\u5185\u5bb9\u5b8c\u5168\u4f9d\u8d56\u4e3b\u52a8\u641c\u7d22\u6216\u5173\u6ce8\u8d26\u53f7\u3002\u968f\u7740\u5e73\u53f0\u5185\u5bb9\u91cf\u7206\u53d1\u5f0f\u589e\u957f，Instagram\u6bcf\u5929\u65b0\u589e\u6570\u767e\u4e07\u6761\u5e16\u5b50，\u4f20\u7edf\u5c55\u793a\u65b9\u5f0f\u5df2\u7ecf\u65e0\u6cd5\u6ee1\u8db3\u7528\u6237\u7684\u4fe1\u606f\u83b7\u53d6\u9700\u6c42，\u63a8\u8350\u7cfb\u7edf\u6210\u4e3a\u5185\u5bb9\u5e73\u53f0\u7684\u6838\u5fc3\u57fa\u7840\u8bbe\u65bd\u3002<\/p>\n

\u4e00\u3001\u63a8\u8350\u7cfb\u7edf\u7684\u6838\u5fc3\u67b6\u6784：\u53ec\u56de-\u6392\u5e8f-\u91cd\u6392\u4e09\u6bb5\u5f0f\u94fe\u8def<\/p>\n

\u73b0\u4ee3\u5de5\u4e1a\u7ea7\u63a8\u8350\u7cfb\u7edf\u666e\u904d\u91c7\u7528\u591a\u9636\u6bb5\u6f0f\u6597\u5f0f\u67b6\u6784，\u901a\u8fc7\u5c42\u5c42\u7b5b\u9009\u5728\u8ba1\u7b97\u6548\u7387\u548c\u63a8\u8350\u7cbe\u5ea6\u4e4b\u95f4\u53d6\u5f97\u5e73\u8861\u3002\u6574\u4f53\u6d41\u7a0b\u5982\u4e0b：<\/p>\n

\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 \u5168\u91cf\u5185\u5bb9\u6c60（\u5343\u4e07\u7ea7） \u2502
\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518
\n\u25bc
\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 \u53ec\u56de\u5c42（Recall） \u2502
\n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502
\n\u2502 \u2502\u534f\u540c\u8fc7\u6ee4 \u2502\u5185\u5bb9\u6807\u7b7e \u2502\u70ed\u5ea6\u53ec\u56de\u2502\u56fe\u7b97\u6cd5 \u2502 \u2502
\n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502
\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518
\n\u25bc
\n\u5019\u9009\u96c6（\u5343\u7ea7）
\n\u25bc
\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 \u7c97\u6392\u5c42（Pre-Ranking） \u2502
\n\u2502 \u8f7b\u91cf\u6a21\u578b\u5feb\u901f\u7b5b\u9009，\u4fdd\u7559\u767e\u7ea7 \u2502
\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518
\n\u25bc
\n\u5019\u9009\u96c6（\u767e\u7ea7）
\n\u25bc
\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 \u7cbe\u6392\u5c42（Ranking） \u2502
\n\u2502 DeepFM \/ DIN \/ SlimPer \u6df1\u5ea6\u6a21\u578b\u6392\u5e8f \u2502
\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518
\n\u25bc
\n\u5019\u9009\u96c6（\u5341\u7ea7）
\n\u25bc
\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 \u91cd\u6392\u5c42（Re-Ranking） \u2502
\n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502
\n\u2502 \u2502 \u53bb\u91cd \u2502\u591a\u6837\u6027\u2502\u5e7f\u544a\u63d2\u5165\u2502\u65b0\u5185\u5bb9\u6276\u6301\u2502\u793e\u4ea4\u5173\u7cfb\u2502 \u2502
\n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502
\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518
\n\u25bc
\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 \u6700\u7ec8\u5c55\u793a\u5185\u5bb9（Feed\u6d41） \u2502
\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518<\/p>\n

\u53ec\u56de\u5c42（Recall）<\/p>\n

\u53ec\u56de\u5c42\u7684\u6838\u5fc3\u76ee\u6807\u662f\u4ece\u5343\u4e07\u7ea7\u5185\u5bb9\u6c60\u4e2d\u5feb\u901f\u7b5b\u9009\u51fa\u5343\u7ea7\u522b\u5019\u9009\u96c6，\u5bf9\u6027\u80fd\u8981\u6c42\u6781\u9ad8，\u901a\u5e38\u8981\u6c42\u6beb\u79d2\u7ea7\u54cd\u5e94\u3002<\/p>\n

\u591a\u8def\u53ec\u56de\u7b56\u7565：
\n| | \u53ec\u56de\u7b56\u7565 | \u6838\u5fc3\u539f\u7406 | \u9002\u7528\u573a\u666f |
\n|———|———|———|
\n| \u534f\u540c\u8fc7\u6ee4\u53ec\u56de | \u76f8\u4f3c\u7528\u6237\u559c\u6b22\u7684\u5185\u5bb9\u63a8\u8350\u7ed9\u76ee\u6807\u7528\u6237 | \u6709\u4e30\u5bcc\u884c\u4e3a\u6570\u636e\u7684\u6d3b\u8dc3\u7528\u6237 |
\n| \u5185\u5bb9\u6807\u7b7e\u53ec\u56de | \u57fa\u4e8e\u7528\u6237\u5386\u53f2\u5174\u8da3\u6807\u7b7e\u5339\u914d\u5185\u5bb9\u7279\u5f81 | \u51b7\u542f\u52a8\u3001\u65b0\u7528\u6237 |
\n| \u70ed\u5ea6\u53ec\u56de | \u5168\u5c40\u70ed\u95e8\u5185\u5bb9\u4f18\u5148\u63a8\u8350 | \u70ed\u95e8\u4e8b\u4ef6\u3001\u7206\u6b3e\u5185\u5bb9 |
\n| \u56fe\u7b97\u6cd5\u53ec\u56de | \u57fa\u4e8e\u793e\u4ea4\u5173\u7cfb\u56fe\u4f20\u64ad\u63a8\u8350 | \u6709\u793e\u4ea4\u5173\u7cfb\u7684\u5e73\u53f0 |
\n| \u5411\u91cf\u53ec\u56de | \u5c06\u7528\u6237\u548c\u5185\u5bb9\u6620\u5c04\u5230\u540c\u4e00\u5411\u91cf\u7a7a\u95f4\u505a\u76f8\u4f3c\u5ea6\u5339\u914d | \u5927\u89c4\u6a21\u5185\u5bb9\u6c60\u9ad8\u6548\u68c0\u7d22 |<\/p>\n

\u7c97\u6392\u5c42（Pre-Ranking）<\/p>\n

\u7c97\u6392\u5c42\u4f7f\u7528\u8f7b\u91cf\u7ea7\u6a21\u578b\u5bf9\u53ec\u56de\u7684\u5343\u7ea7\u5019\u9009\u96c6\u8fdb\u884c\u5feb\u901f\u6253\u5206\u6392\u5e8f，\u7b5b\u9009\u51fa\u767e\u7ea7\u5019\u9009\u96c6\u8fdb\u5165\u7cbe\u6392\u5c42\u3002\u5e38\u7528\u6a21\u578b\u5305\u62ec：<\/p>\n

\u6d45\u5c42\u795e\u7ecf\u7f51\u7edc（2-3\u5c42MLP）
\n\u53cc\u5854\u6a21\u578b（\u7528\u6237\u5854 + \u7269\u54c1\u5854）
\n\u57fa\u4e8e\u7279\u5f81\u4ea4\u53c9\u7684\u8f7b\u91cf\u6a21\u578b<\/p>\n

\u7cbe\u6392\u5c42（Ranking）<\/p>\n

\u7cbe\u6392\u5c42\u662f\u63a8\u8350\u7cfb\u7edf\u7684\u6838\u5fc3，\u4f7f\u7528\u590d\u6742\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u5bf9\u767e\u7ea7\u5019\u9009\u96c6\u8fdb\u884c\u7cbe\u51c6\u6253\u5206\u6392\u5e8f\u3002\u4e3b\u6d41\u6a21\u578b\u5305\u62ec：<\/p>\n

DeepFM：\u7ed3\u5408\u56e0\u5b50\u5206\u89e3\u673a\u548c\u6df1\u5ea6\u7f51\u7edc，\u540c\u65f6\u6355\u83b7\u4f4e\u9636\u548c\u9ad8\u9636\u7279\u5f81\u4ea4\u53c9
\nDIN（Deep Interest Network）：\u901a\u8fc7\u6ce8\u610f\u529b\u673a\u5236\u5efa\u6a21\u7528\u6237\u5386\u53f2\u884c\u4e3a\u4e0e\u5019\u9009\u5185\u5bb9\u7684\u76f8\u5173\u6027
\nSlimPer：Meta\u81ea\u7814\u7684\u8f7b\u91cf\u7ea7\u7cbe\u6392\u6a21\u578b，\u5728\u4fdd\u6301\u7cbe\u5ea6\u7684\u540c\u65f6\u5927\u5e45\u964d\u4f4e\u63a8\u7406\u5ef6\u8fdf<\/p>\n

\u91cd\u6392\u5c42（Re-Ranking）<\/p>\n

\u91cd\u6392\u5c42\u5728\u7cbe\u6392\u7ed3\u679c\u57fa\u7840\u4e0a\u8fdb\u884c\u4e1a\u52a1\u89c4\u5219\u8c03\u6574：<\/p>\n

\u53bb\u91cd：\u907f\u514d\u540c\u4e00\u6765\u6e90\u5185\u5bb9\u91cd\u590d\u51fa\u73b0
\n\u591a\u6837\u6027\u6253\u6563：\u540c\u7c7b\u578b\u5185\u5bb9\u95f4\u9694\u5c55\u793a，\u907f\u514d\u4fe1\u606f\u8327\u623f
\n\u5e7f\u544a\u63d2\u5165：\u5728\u81ea\u7136\u5185\u5bb9\u4e2d\u7a7f\u63d2\u5e7f\u544a\u4f4d
\n\u65b0\u5185\u5bb9\u6276\u6301：\u7ed9\u65b0\u53d1\u5e03\u5185\u5bb9\u4e00\u5b9a\u66dd\u5149\u673a\u4f1a
\n\u793e\u4ea4\u5173\u7cfb\u52a0\u6743：\u597d\u53cb\u4e92\u52a8\u8fc7\u7684\u5185\u5bb9\u63d0\u5347\u6392\u5e8f<\/p>\n

\u4e8c\u3001\u7528\u6237\u5174\u8da3\u5efa\u6a21：\u884c\u4e3a\u4fe1\u53f7\u4e0e\u5174\u8da3\u5411\u91cf<\/p>\n

\u63a8\u8350\u7cfb\u7edf\u7684\u7cbe\u5ea6\u53d6\u51b3\u4e8e\u5bf9\u7528\u6237\u5174\u8da3\u7684\u7406\u89e3\u6df1\u5ea6\u3002\u73b0\u4ee3\u63a8\u8350\u7cfb\u7edf\u5c06\u7528\u6237\u5174\u8da3\u8868\u793a\u4e3a\u9ad8\u7ef4\u5411\u91cf，\u901a\u8fc7\u6301\u7eed\u7684\u884c\u4e3a\u4fe1\u53f7\u8fdb\u884c\u52a8\u6001\u66f4\u65b0\u3002<\/p>\n

\u7528\u6237\u884c\u4e3a\u4fe1\u53f7\u6743\u91cd\u4f53\u7cfb<\/p>\n

\u4e0d\u540c\u7528\u6237\u884c\u4e3a\u5bf9\u5174\u8da3\u5224\u65ad\u7684\u4ef7\u503c\u4e0d\u540c：<\/p>\n\n\u884c\u4e3a\u7c7b\u578b\u6743\u91cd\u7cfb\u6570\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n\n\n
\u5b8c\u64ad\/\u957f\u505c\u7559<\/td>\n1.0<\/td>\n\u5f3a\u6b63\u4fe1\u53f7，\u8bf4\u660e\u5185\u5bb9\u771f\u6b63\u5438\u5f15\u7528\u6237<\/td>\n<\/tr>\n
\u5206\u4eab\/\u8f6c\u53d1<\/td>\n0.9<\/td>\n\u5f3a\u6b63\u4fe1\u53f7，\u7528\u6237\u613f\u610f\u80cc\u4e66<\/td>\n<\/tr>\n
\u6536\u85cf\/\u4fdd\u5b58<\/td>\n0.8<\/td>\n\u5f3a\u6b63\u4fe1\u53f7，\u6709\u590d\u770b\u610f\u56fe<\/td>\n<\/tr>\n
\u70b9\u8d5e<\/td>\n0.5<\/td>\n\u4e2d\u7b49\u6b63\u4fe1\u53f7，\u8ba4\u53ef\u4f46\u6210\u672c\u4f4e<\/td>\n<\/tr>\n
\u8bc4\u8bba<\/td>\n0.5<\/td>\n\u4e2d\u7b49\u6b63\u4fe1\u53f7，\u6709\u4e92\u52a8\u610f\u613f<\/td>\n<\/tr>\n
\u5feb\u901f\u8df3\u8fc7<\/td>\n-0.3<\/td>\n\u8d1f\u4fe1\u53f7，\u4e0d\u611f\u5174\u8da3<\/td>\n<\/tr>\n
\u70b9\u51fb"\u4e0d\u611f\u5174\u8da3"<\/td>\n-1.0<\/td>\n\u5f3a\u8d1f\u4fe1\u53f7，\u660e\u786e\u62d2\u7edd<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u7528\u6237\u5174\u8da3\u5411\u91cf\u66f4\u65b0\u903b\u8f91<\/p>\n

\u4ee5\u4e0b\u662f\u7528\u6237\u5174\u8da3\u5411\u91cf\u7684\u6838\u5fc3\u66f4\u65b0\u903b\u8f91：<\/p>\n

def update_user_profile(user_profile, behavior_event):
\n\u201c\u201d"
\n\u6839\u636e\u7528\u6237\u884c\u4e3a\u4e8b\u4ef6\u66f4\u65b0\u5174\u8da3\u753b\u50cf\u5411\u91cf<\/p>\n

\u53c2\u6570:
\n user_profile: \u7528\u6237\u5f53\u524d\u5174\u8da3\u5411\u91cf，key\u4e3a\u5185\u5bb9\u6807\u7b7e，value\u4e3a\u5174\u8da3\u6743\u91cd
\n behavior_event: \u884c\u4e3a\u4e8b\u4ef6\u5bf9\u8c61，\u5305\u542bcontent_tag(\u5185\u5bb9\u6807\u7b7e)\u3001
\n behavior_type(\u884c\u4e3a\u7c7b\u578b)\u3001duration(\u505c\u7559\u65f6\u957f)
\n"""<\/p>\n

# \u4e0d\u540c\u884c\u4e3a\u5bf9\u5e94\u7684\u6743\u91cd\u7cfb\u6570
\nbehavior_weight = {
\n "share": 1.0, # \u5206\u4eab：\u5f3a\u6b63\u4fe1\u53f7
\n "save": 0.8, # \u6536\u85cf：\u5f3a\u6b63\u4fe1\u53f7
\n "complete_watch": 0.7, # \u5b8c\u64ad：\u5f3a\u6b63\u4fe1\u53f7
\n "like": 0.5, # \u70b9\u8d5e：\u4e2d\u7b49\u6b63\u4fe1\u53f7
\n "comment": 0.5, # \u8bc4\u8bba：\u4e2d\u7b49\u6b63\u4fe1\u53f7
\n "quick_skip": -0.3, # \u5feb\u901f\u8df3\u8fc7：\u8d1f\u4fe1\u53f7
\n "not_interested": -1.0, # \u660e\u786e\u4e0d\u611f\u5174\u8da3：\u5f3a\u8d1f\u4fe1\u53f7
\n}<\/p>\n

# \u83b7\u53d6\u884c\u4e3a\u6743\u91cd
\nweight = behavior_weight.get(behavior_event.behavior_type, 0.0)<\/p>\n

# \u7ed3\u5408\u505c\u7559\u65f6\u957f\u7684\u8870\u51cf\u7cfb\u6570（\u505c\u7559\u8d8a\u957f，\u4fe1\u53f7\u8d8a\u5f3a）
\nduration_factor = min(behavior_event.duration \/ 60.0, 1.0)<\/p>\n

# \u66f4\u65b0\u5bf9\u5e94\u6807\u7b7e\u7684\u5174\u8da3\u6743\u91cd
\ntag = behavior_event.content_tag
\nuser_profile[tag] += weight * duration_factor<\/p>\n

# \u5174\u8da3\u8870\u51cf：\u6240\u6709\u6807\u7b7e\u7684\u5174\u8da3\u6743\u91cd\u968f\u65f6\u95f4\u8870\u51cf，\u907f\u514d\u5386\u53f2\u884c\u4e3a\u8fc7\u5ea6\u5f71\u54cd
\ndecay_rate = 0.95
\nuser_profile = {k: v * decay_rate for k, v in user_profile.items()}<\/p>\n

# \u786e\u4fdd\u6743\u91cd\u5728\u5408\u7406\u8303\u56f4\u5185
\nuser_profile = {k: max(-1.0, min(1.0, v)) for k, v in user_profile.items()}<\/p>\n

return user_profile<\/p>\n

\u534f\u540c\u8fc7\u6ee4\u7b97\u6cd5\u539f\u7406<\/p>\n

\u534f\u540c\u8fc7\u6ee4（Collaborative Filtering）\u662f\u63a8\u8350\u7cfb\u7edf\u6700\u7ecf\u5178\u7684\u7b97\u6cd5\u4e4b\u4e00，\u6838\u5fc3\u601d\u60f3\u662f"\u76f8\u4f3c\u7684\u7528\u6237\u559c\u6b22\u76f8\u4f3c\u7684\u5185\u5bb9"\u3002<\/p>\n

\u57fa\u4e8e\u7528\u6237\u7684\u534f\u540c\u8fc7\u6ee4（User-Based CF）：<\/p>\n

import numpy as np
\nfrom sklearn.metrics.pairwise import cosine_similarity<\/p>\n

def user_based_cf(user_item_matrix, target_user, top_k=10):
\n\u201c\u201d"
\n\u57fa\u4e8e\u7528\u6237\u7684\u534f\u540c\u8fc7\u6ee4\u63a8\u8350<\/p>\n

\u53c2\u6570:
\n user_item_matrix: \u7528\u6237-\u7269\u54c1\u4ea4\u4e92\u77e9\u9635（\u884c：\u7528\u6237，\u5217：\u7269\u54c1，\u503c：\u8bc4\u5206\/\u4ea4\u4e92\u6b21\u6570）
\n target_user: \u76ee\u6807\u7528\u6237\u7d22\u5f15
\n top_k: \u8fd4\u56de\u63a8\u8350\u7269\u54c1\u6570\u91cf
\n"""<\/p>\n

# \u8ba1\u7b97\u7528\u6237\u4e4b\u95f4\u7684\u4f59\u5f26\u76f8\u4f3c\u5ea6
\nuser_similarities = cosine_similarity(user_item_matrix)<\/p>\n

# \u83b7\u53d6\u4e0e\u76ee\u6807\u7528\u6237\u6700\u76f8\u4f3c\u7684K\u4e2a\u7528\u6237
\ntarget_sim = user_similarities[target_user]
\ntarget_sim[target_user] = 0 # \u6392\u9664\u81ea\u5df1<\/p>\n

# \u627e\u5230\u6700\u76f8\u4f3c\u7684\u90bb\u5c45\u7528\u6237
\nk_neighbors = np.argsort(target_sim)[::-1][:50]<\/p>\n

# \u805a\u5408\u90bb\u5c45\u7528\u6237\u4ea4\u4e92\u8fc7\u7684\u7269\u54c1
\nitem_scores = {}
\nfor neighbor in k_neighbors:
\n sim_score = target_sim[neighbor]
\n # \u83b7\u53d6\u90bb\u5c45\u7528\u6237\u4ea4\u4e92\u8fc7\u7684\u7269\u54c1
\n interacted_items = np.where(user_item_matrix[neighbor] > 0)[0]
\n for item in interacted_items:
\n if user_item_matrix[target_user, item] == 0: # \u76ee\u6807\u7528\u6237\u672a\u4ea4\u4e92\u8fc7
\n item_scores[item] = item_scores.get(item, 0) + sim_score * user_item_matrix[neighbor, item]<\/p>\n

# \u6309\u63a8\u8350\u5206\u6570\u6392\u5e8f
\nsorted_items = sorted(item_scores.items(), key=lambda x: x[1], reverse=True)<\/p>\n

return [item for item, score in sorted_items[:top_k]]<\/p>\n

\u57fa\u4e8e\u7269\u54c1\u7684\u534f\u540c\u8fc7\u6ee4（Item-Based CF）：<\/p>\n

def item_based_cf(user_item_matrix, user_history_items, top_k=10):
\n\u201c\u201d"
\n\u57fa\u4e8e\u7269\u54c1\u7684\u534f\u540c\u8fc7\u6ee4\u63a8\u8350<\/p>\n

\u53c2\u6570:
\n user_item_matrix: \u7528\u6237-\u7269\u54c1\u4ea4\u4e92\u77e9\u9635
\n user_history_items: \u7528\u6237\u5386\u53f2\u4ea4\u4e92\u8fc7\u7684\u7269\u54c1\u5217\u8868
\n top_k: \u8fd4\u56de\u63a8\u8350\u7269\u54c1\u6570\u91cf
\n"""<\/p>\n

# \u8ba1\u7b97\u7269\u54c1\u4e4b\u95f4\u7684\u4f59\u5f26\u76f8\u4f3c\u5ea6（\u5bf9\u77e9\u9635\u8f6c\u7f6e\u540e\u8ba1\u7b97）
\nitem_similarities = cosine_similarity(user_item_matrix.T)<\/p>\n

# \u5bf9\u5019\u9009\u7269\u54c1\u6253\u5206
\nitem_scores = {}
\nnum_items = user_item_matrix.shape[1]<\/p>\n

for candidate_item in range(num_items):
\n if candidate_item in user_history_items:
\n continue # \u5df2\u4ea4\u4e92\u8fc7\u7684\u7269\u54c1\u8df3\u8fc7<\/p>\n

score = 0
\n for history_item in user_history_items:
\n score += item_similarities[candidate_item, history_item]<\/p>\n

item_scores[candidate_item] = score<\/p>\n

sorted_items = sorted(item_scores.items(), key=lambda x: x[1], reverse=True)
\nreturn [item for item, score in sorted_items[:top_k]]<\/p>\n

\u4e09\u3001\u6df1\u5ea6\u5b66\u4e60\u6392\u5e8f\u6a21\u578b：\u4eceDeepFM\u5230DIN<\/p>\n

\u7cbe\u6392\u5c42\u4f7f\u7528\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u662f\u63a8\u8350\u7cfb\u7edf\u6280\u672f\u542b\u91cf\u6700\u9ad8\u7684\u90e8\u5206，\u4e0b\u9762\u4ecb\u7ecd\u4e09\u79cd\u4e3b\u6d41\u6a21\u578b\u7684\u6838\u5fc3\u539f\u7406\u3002<\/p>\n

DeepFM：\u56e0\u5b50\u5206\u89e3\u673a + \u6df1\u5ea6\u7f51\u7edc\u7684\u878d\u5408<\/p>\n

DeepFM\u7684\u6838\u5fc3\u521b\u65b0\u5728\u4e8e\u540c\u65f6\u6355\u83b7\u4f4e\u9636\u7279\u5f81\u7ec4\u5408\u548c\u9ad8\u9636\u7279\u5f81\u7ec4\u5408：<\/p>\n

import tensorflow as tf
\nfrom tensorflow.keras.layers import Dense, Embedding, Flatten, concatenate
\nfrom tensorflow.keras.models import Model<\/p>\n

class DeepFM(Model):
\n\u201c\u201d"
\nDeepFM\u6a21\u578b：FM\u90e8\u5206\u6355\u83b7\u4f4e\u9636\u7279\u5f81\u4ea4\u53c9，Deep\u90e8\u5206\u6355\u83b7\u9ad8\u9636\u7279\u5f81\u4ea4\u53c9
\n\u201c\u201d"
\ndef init(self, num_fields, embed_dim, deep_layers):
\nsuper(DeepFM, self).init()
\nself.num_fields = num_fields # \u7279\u5f81\u5b57\u6bb5\u6570\u91cf
\nself.embed_dim = embed_dim # \u5d4c\u5165\u7ef4\u5ea6<\/p>\n

# \u5d4c\u5165\u5c42
\n self.embedding = Embedding(input_dim=10000, output_dim=embed_dim)<\/p>\n

# FM\u90e8\u5206：\u4e00\u9636\u7ebf\u6027\u90e8\u5206 + \u4e8c\u9636\u4ea4\u53c9\u90e8\u5206
\n self.fm_first_order = Dense(1, use_bias=True)<\/p>\n

# Deep\u90e8\u5206：\u591a\u5c42\u611f\u77e5\u673a
\n self.deep_layers_list = []
\n prev_dim = num_fields * embed_dim
\n for layer_dim in deep_layers:
\n self.deep_layers_list.append(Dense(layer_dim, activation='relu'))
\n prev_dim = layer_dim<\/p>\n

# \u6700\u7ec8\u8f93\u51fa\u5c42
\n self.output_layer = Dense(1, activation='sigmoid')<\/p>\n

def call(self, inputs):
\n # inputs: [batch_size, num_fields]
\n embeds = self.embedding(inputs) # [batch_size, num_fields, embed_dim]<\/p>\n

# FM\u4e00\u9636\u90e8\u5206
\n fm_first = self.fm_first_order(Flatten()(embeds))<\/p>\n

# FM\u4e8c\u9636\u90e8\u5206（\u7b80\u5316\u7248）
\n sum_embeds = tf.reduce_sum(embeds, axis=1)
\n sum_square_embeds = tf.square(sum_embeds)
\n square_sum_embeds = tf.reduce_sum(tf.square(embeds), axis=1)
\n fm_second = 0.5 * tf.reduce_sum(sum_square_embeds – square_sum_embeds, axis=1, keepdims=True)<\/p>\n

# Deep\u90e8\u5206
\n deep_input = Flatten()(embeds)
\n deep_out = deep_input
\n for layer in self.deep_layers_list:
\n deep_out = layer(deep_out)<\/p>\n

# \u878d\u5408FM\u548cDeep\u7684\u8f93\u51fa
\n combined = concatenate([fm_first, fm_second, deep_out])
\n output = self.output_layer(combined)<\/p>\n

return output<\/p>\n

DIN（Deep Interest Network）：\u6ce8\u610f\u529b\u673a\u5236\u5efa\u6a21\u7528\u6237\u5174\u8da3<\/p>\n

DIN\u7684\u6838\u5fc3\u521b\u65b0\u662f\u5f15\u5165\u6ce8\u610f\u529b\u673a\u5236，\u6839\u636e\u5019\u9009\u7269\u54c1\u52a8\u6001\u6fc0\u6d3b\u7528\u6237\u5386\u53f2\u884c\u4e3a\u4e2d\u7684\u76f8\u5173\u90e8\u5206：<\/p>\n

import tensorflow as tf
\nfrom tensorflow.keras.layers import Dense, Concatenate, Multiply, Lambda<\/p>\n

def dice(x, name=\u2018dice\u2019):
\n\u201c\u201d"
\nDIN\u4e2d\u4f7f\u7528\u7684DICE\u6fc0\u6d3b\u51fd\u6570（Data Adaptive Activation Function）
\n\u6bd4\u4f20\u7edfReLU\u66f4\u9002\u5408\u63a8\u8350\u573a\u666f\u7684\u7a00\u758f\u7279\u5f81
\n\u201c\u201d"
\nalpha = tf.Variable(tf.zeros(x.shape[-1:]), name=name + \u2018_alpha\u2019)
\nps = tf.nn.sigmoid(x)
\nreturn ps * x + (1 – ps) * alpha * x<\/p>\n

def attention_layer(query, keys, values, keys_length):
\n\u201c\u201d"
\n\u5c40\u90e8\u6ce8\u610f\u529b\u5c42：\u6839\u636e\u5019\u9009\u7269\u54c1(query)\u8ba1\u7b97\u7528\u6237\u5386\u53f2\u884c\u4e3a(keys)\u7684\u6ce8\u610f\u529b\u6743\u91cd<\/p>\n

\u53c2\u6570:
\n query: \u5019\u9009\u7269\u54c1\u5411\u91cf [batch_size, embed_dim]
\n keys: \u7528\u6237\u5386\u53f2\u884c\u4e3a\u7269\u54c1\u5411\u91cf [batch_size, history_len, embed_dim]
\n values: \u7528\u6237\u5386\u53f2\u884c\u4e3a\u7269\u54c1\u5411\u91cf（\u901a\u5e38\u4e0ekeys\u76f8\u540c）
\n keys_length: \u6bcf\u4e2a\u7528\u6237\u5b9e\u9645\u5386\u53f2\u884c\u4e3a\u957f\u5ea6
\n"""
\nhistory_len = tf.shape(keys)[1]
\nquery_expand = tf.tile(tf.expand_dims(query, 1), [1, history_len, 1])<\/p>\n

# \u8ba1\u7b97\u6ce8\u610f\u529b\u5206\u6570：[query, keys, query-keys, query*keys]
\ndin_unit = Concatenate(axis=-1)([
\n query_expand,
\n keys,
\n query_expand – keys,
\n query_expand * keys # \u9010\u5143\u7d20\u4e58\u6cd5（element-wise multiply）
\n])<\/p>\n

# \u901a\u8fc7MLP\u8ba1\u7b97\u6ce8\u610f\u529b\u6743\u91cd
\nattention_scores = Dense(80, activation='relu')(din_unit)
\nattention_scores = Dense(40, activation='relu')(attention_scores)
\nattention_scores = Dense(1, activation=None)(attention_scores)
\nattention_scores = tf.squeeze(attention_scores, axis=-1) # [batch_size, history_len]<\/p>\n

# \u63a9\u7801：\u5c06padding\u4f4d\u7f6e\u8bbe\u4e3a\u8d1f\u65e0\u7a77
\nmask = tf.sequence_mask(keys_length, maxlen=history_len)
\npaddings = tf.ones_like(attention_scores) * (-2 ** 32 + 1)
\nattention_scores = tf.where(mask, attention_scores, paddings)<\/p>\n

# Softmax\u5f52\u4e00\u5316
\nattention_weights = tf.nn.softmax(attention_scores) # [batch_size, history_len]<\/p>\n

# \u52a0\u6743\u6c42\u548c
\noutput = tf.reduce_sum(tf.expand_dims(attention_weights, -1) * values, axis=1)<\/p>\n

return output, attention_weights<\/p>\n

\u56db\u3001LLM\u4e0e\u5927\u6a21\u578b\u5728\u63a8\u8350\u7cfb\u7edf\u4e2d\u7684\u5e94\u7528<\/p>\n

2025\u20142026\u5e74，\u5927\u8bed\u8a00\u6a21\u578b（LLM）\u5f00\u59cb\u6df1\u5ea6\u878d\u5165\u63a8\u8350\u7cfb\u7edf，\u4e3b\u8981\u4f53\u73b0\u5728\u4ee5\u4e0b\u4e09\u4e2a\u65b9\u9762：<\/p>\n

\u5185\u5bb9\u7406\u89e3\u4e0e\u7279\u5f81\u63d0\u53d6<\/p>\n

\u4f20\u7edf\u63a8\u8350\u7cfb\u7edf\u4f9d\u8d56\u4eba\u5de5\u6807\u6ce8\u7684\u5185\u5bb9\u6807\u7b7e，\u800cLLM\u53ef\u4ee5\u81ea\u52a8\u7406\u89e3\u5185\u5bb9\u7684\u6df1\u5c42\u8bed\u4e49：<\/p>\n

\u4f7f\u7528LLM\u8fdb\u884c\u5185\u5bb9\u7279\u5f81\u63d0\u53d6\u7684\u793a\u4f8b\u6d41\u7a0b<\/p>\n

\u4ee5\u4e0b\u4e3a\u4f2a\u4ee3\u7801，\u4ec5\u5c55\u793a\u903b\u8f91\u6d41\u7a0b<\/h2>\n

def extract_content_features_with_llm(content_text, content_image=None):
\n\u201c\u201d"
\n\u4f7f\u7528\u5927\u8bed\u8a00\u6a21\u578b\u63d0\u53d6\u5185\u5bb9\u7279\u5f81<\/p>\n

\u6d41\u7a0b:
\nLLM\u7406\u89e3\u6587\u672c\u5185\u5bb9\u7684\u4e3b\u9898\u3001\u60c5\u611f\u3001\u98ce\u683c
\n\u591a\u6a21\u6001\u6a21\u578b\u7406\u89e3\u56fe\u7247\/\u89c6\u9891\u7684\u89c6\u89c9\u7279\u5f81
\n\u8f93\u51fa\u7ed3\u6784\u5316\u7684\u5185\u5bb9\u7279\u5f81\u5411\u91cf
\n"""<\/p>\n

# Step 1: \u6587\u672c\u7279\u5f81\u63d0\u53d6
\ntext_prompt = f"""
\n\u8bf7\u5206\u6790\u4ee5\u4e0b\u5185\u5bb9\u7684\u7279\u5f81，\u8fd4\u56deJSON\u683c\u5f0f：
\n\u4e3b\u9898\u5206\u7c7b（\u6700\u591a3\u4e2a）
\n\u60c5\u611f\u503e\u5411（positive\/neutral\/negative）
\n\u5185\u5bb9\u98ce\u683c（\u641e\u7b11\/\u6559\u80b2\/\u5a31\u4e50\/\u65b0\u95fb\u7b49）
\n\u76ee\u6807\u53d7\u4f17（\u5e74\u9f84\u6bb5\u3001\u5174\u8da3\u7fa4\u4f53）
\n\u5173\u952e\u8bcd（\u6700\u591a5\u4e2a）<\/p>\n

\u5185\u5bb9: {content_text}
\n"""<\/p>\n

# \u8c03\u7528LLM API（\u4f2a\u4ee3\u7801）
\nllm_response = call_llm_api(text_prompt)
\nstructured_features = parse_json(llm_response)<\/p>\n

# Step 2: \u5c06\u7ed3\u6784\u5316\u7279\u5f81\u6620\u5c04\u5230\u63a8\u8350\u7cfb\u7edf\u7684\u7279\u5f81\u5411\u91cf
\nfeature_vector = map_to_embedding(structured_features)<\/p>\n

return feature_vector<\/p>\n

\u751f\u6210\u5f0f\u63a8\u8350（Generative Recommendation）<\/p>\n

Meta\u57282024\u5e74\u63a8\u51fa\u4e86SlimPer\u548c\u751f\u6210\u5f0f\u63a8\u8350\u6846\u67b6，\u5c06\u63a8\u8350\u95ee\u9898\u8f6c\u5316\u4e3a\u751f\u6210\u95ee\u9898：<\/p>\n

\u751f\u6210\u5f0f\u63a8\u8350\u7684\u6838\u5fc3\u601d\u8def：\u5c06\u63a8\u8350\u8f6c\u5316\u4e3a\u5e8f\u5217\u751f\u6210<\/p>\n

\u4ee5\u4e0b\u4e3a\u4f2a\u4ee3\u7801，\u4ec5\u5c55\u793a\u903b\u8f91\u6d41\u7a0b<\/h2>\n

def generative_recommendation(user_history, context_features, llm_model):
\n\u201c\u201d"
\n\u751f\u6210\u5f0f\u63a8\u8350：\u4f7f\u7528LLM\u76f4\u63a5\u751f\u6210\u63a8\u8350\u5185\u5bb9ID\u5e8f\u5217<\/p>\n

\u4e0e\u4f20\u7edf"\u53ec\u56de-\u6392\u5e8f"\u8303\u5f0f\u4e0d\u540c，\u751f\u6210\u5f0f\u63a8\u8350\u5c06\u7528\u6237\u5386\u53f2\u884c\u4e3a\u4f5c\u4e3aprompt，
\n\u8ba9LLM\u76f4\u63a5\u751f\u6210\u4e0b\u4e00\u4e2a\u6700\u53ef\u80fd\u611f\u5174\u8da3\u7684\u5185\u5bb9ID
\n"""<\/p>\n

# \u6784\u5efaprompt
\nprompt = build_recommendation_prompt(
\n user_history=user_history, # \u7528\u6237\u5386\u53f2\u4ea4\u4e92\u5e8f\u5217
\n context=context_features, # \u5f53\u524d\u4e0a\u4e0b\u6587（\u65f6\u95f4\u3001\u4f4d\u7f6e\u7b49）
\n task="predict_next_item" # \u4efb\u52a1\u7c7b\u578b
\n)<\/p>\n

# LLM\u751f\u6210\u63a8\u8350\u5185\u5bb9ID
\ngenerated_item_ids = llm_model.generate(
\n prompt,
\n max_new_tokens=10,
\n temperature=0.7
\n)<\/p>\n

return generated_item_ids<\/p>\n

\u7528\u6237\u753b\u50cf\u4e0e\u610f\u56fe\u7406\u89e3<\/p>\n

LLM\u53ef\u4ee5\u66f4\u6df1\u5165\u5730\u7406\u89e3\u7528\u6237\u610f\u56fe，\u800c\u4e0d\u4ec5\u4ec5\u4f9d\u8d56\u884c\u4e3a\u7edf\u8ba1：<\/p>\n

\u4ee5\u4e0b\u4e3a\u4f2a\u4ee3\u7801，\u4ec5\u5c55\u793a\u903b\u8f91\u6d41\u7a0b<\/h2>\n

def enhance_user_profile_with_llm(user_behaviors, user_profile):
\n\u201c\u201d"
\n\u4f7f\u7528LLM\u589e\u5f3a\u7528\u6237\u753b\u50cf：\u4ece\u884c\u4e3a\u5e8f\u5217\u4e2d\u63a8\u65ad\u6df1\u5c42\u5174\u8da3<\/p>\n

\u8f93\u5165:
\n user_behaviors: \u7528\u6237\u6700\u8fd1N\u6761\u884c\u4e3a\u8bb0\u5f55
\n user_profile: \u73b0\u6709\u7528\u6237\u753b\u50cf\u5411\u91cf<\/p>\n

\u8f93\u51fa:
\n enhanced_profile: \u589e\u5f3a\u540e\u7684\u7528\u6237\u753b\u50cf
\n"""<\/p>\n

# \u6784\u5efa\u7528\u6237\u884c\u4e3a\u6458\u8981prompt
\nbehavior_summary = ""
\nfor b in user_behaviors[-20:]: # \u6700\u8fd120\u6761\u884c\u4e3a
\n behavior_summary += f"[{b.timestamp}] {b.action}\u4e86\u5173\u4e8e{b.topic}\u7684\u5185\u5bb9n"<\/p>\n

prompt = f"""
\n\u6839\u636e\u4ee5\u4e0b\u7528\u6237\u884c\u4e3a\u8bb0\u5f55，\u63a8\u65ad\u8be5\u7528\u6237\u7684：
\n\u5f53\u524d\u4e3b\u8981\u5174\u8da3\u65b9\u5411（\u6700\u591a3\u4e2a）
\n\u5174\u8da3\u53d8\u5316\u8d8b\u52bf（\u65b0\u589e\u5174\u8da3\/\u51cf\u5f31\u5174\u8da3）
\n\u5185\u5bb9\u504f\u597d\u98ce\u683c<\/p>\n

\u884c\u4e3a\u8bb0\u5f55:
\n{behavior_summary}
\n"""<\/p>\n

llm_insights = call_llm_api(prompt)<\/p>\n

# \u5c06LLM\u63a8\u65ad\u7684\u5174\u8da3\u878d\u5165\u7528\u6237\u753b\u50cf\u5411\u91cf
\nenhanced_profile = merge_llm_insights(user_profile, llm_insights)<\/p>\n

return enhanced_profile<\/p>\n

\u4e94\u3001\u7528\u6237\u53ef\u63a7\u63a8\u8350：Your Algorithm\u529f\u80fd<\/p>\n

2025\u5e74，Instagram\u63a8\u51fa\u4e86Your Algorithm\u529f\u80fd，\u5c06\u63a8\u8350\u7cfb\u7edf\u7684\u63a7\u5236\u6743\u90e8\u5206\u4ea4\u8fd8\u7ed9\u7528\u6237\u3002\u8fd9\u4e00\u529f\u80fd\u7684\u6280\u672f\u5b9e\u73b0\u5982\u4e0b：<\/p>\n

\u529f\u80fd\u67b6\u6784<\/p>\n\n\u7528\u6237\u64cd\u4f5c\u7cfb\u7edf\u54cd\u5e94\u6280\u672f\u5b9e\u73b0<\/tr>\n\n\n\n\n\n
\u8c03\u6574\u5174\u8da3\u6ed1\u5757（\u5982"\u66f4\u591a\u65c5\u884c\u5185\u5bb9"）<\/td>\n\u63d0\u5347\u5bf9\u5e94\u6807\u7b7e\u7684\u53ec\u56de\u6743\u91cd<\/td>\n\u53ec\u56de\u5c42\u6743\u91cd\u7cfb\u6570\u52a8\u6001\u8c03\u6574<\/td>\n<\/tr>\n
\u5c4f\u853d\u67d0\u7c7b\u5185\u5bb9<\/td>\n\u5728\u91cd\u6392\u5c42\u8fc7\u6ee4\u8be5\u7c7b\u5185\u5bb9<\/td>\n\u91cd\u6392\u5c42\u89c4\u5219\u5f15\u64ce<\/td>\n<\/tr>\n
\u9009\u62e9"\u63a2\u7d22\u65b0\u5185\u5bb9"\u6a21\u5f0f<\/td>\n\u964d\u4f4e\u5386\u53f2\u884c\u4e3a\u6743\u91cd，\u589e\u52a0\u591a\u6837\u6027<\/td>\n\u5174\u8da3\u5411\u91cf\u8870\u51cf\u52a0\u901f + \u591a\u6837\u6027\u91c7\u6837<\/td>\n<\/tr>\n
\u9009\u62e9"\u53ea\u770b\u5173\u6ce8\u7684\u4eba"<\/td>\n\u5207\u6362\u5230\u793e\u4ea4\u5173\u7cfb\u53ec\u56de\u4e3b\u901a\u8def<\/td>\n\u53ec\u56de\u7b56\u7565\u5207\u6362<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u6280\u672f\u5b9e\u73b0\u539f\u7406<\/p>\n

def apply_user_preferences(feed_items, user_preferences):
\n\u201c\u201d"
\n\u6839\u636e\u7528\u6237\u504f\u597d\u8bbe\u7f6e\u8c03\u6574\u63a8\u8350\u7ed3\u679c<\/p>\n

\u53c2\u6570:
\n feed_items: \u63a8\u8350\u5019\u9009\u5185\u5bb9\u5217\u8868
\n user_preferences: \u7528\u6237\u504f\u597d\u8bbe\u7f6e（\u5174\u8da3\u6743\u91cd\u3001\u5c4f\u853d\u5217\u8868\u7b49）
\n"""<\/p>\n

adjusted_items = []<\/p>\n

for item in feed_items:
\n score = item.original_score<\/p>\n

# 1. \u5174\u8da3\u6743\u91cd\u8c03\u6574
\n if item.topic in user_preferences.interest_boosts:
\n score *= (1 + user_preferences.interest_boosts[item.topic])<\/p>\n

# 2. \u5c4f\u853d\u8fc7\u6ee4
\n if item.topic in user_preferences.blocked_topics:
\n continue<\/p>\n

# 3. \u591a\u6837\u6027\u63a7\u5236
\n if user_preferences.exploration_mode:
\n # \u63a2\u7d22\u6a21\u5f0f：\u964d\u4f4e\u70ed\u95e8\u5185\u5bb9\u6743\u91cd，\u63d0\u5347\u65b0\u9896\u5185\u5bb9
\n score = score * 0.7 + item.novelty_score * 0.3<\/p>\n

# 4. \u793e\u4ea4\u5173\u7cfb\u52a0\u6743
\n if user_preferences.following_only:
\n if not item.is_from_following:
\n continue
\n score *= 1.5<\/p>\n

item.adjusted_score = score
\n adjusted_items.append(item)<\/p>\n

# \u6309\u8c03\u6574\u540e\u5206\u6570\u91cd\u65b0\u6392\u5e8f
\nadjusted_items.sort(key=lambda x: x.adjusted_score, reverse=True)<\/p>\n

return adjusted_items<\/p>\n

\u516d\u3001\u63a8\u8350\u7cfb\u7edf\u7684\u8bc4\u4f30\u6307\u6807<\/p>\n

\u63a8\u8350\u7cfb\u7edf\u7684\u6548\u679c\u8bc4\u4f30\u662f\u6301\u7eed\u4f18\u5316\u7684\u57fa\u7840，\u4e3b\u8981\u6307\u6807\u5305\u62ec：<\/p>\n

\u79bb\u7ebf\u8bc4\u4f30\u6307\u6807
\n\u6307\u6807 \u542b\u4e49 \u9002\u7528\u573a\u666f<\/p>\n\n\u6307\u6807\u542b\u4e49\u9002\u7528\u573a\u666f<\/tr>\n\n\n\n\n\n
AUC<\/td>\n\u6a21\u578b\u533a\u5206\u6b63\u8d1f\u6837\u672c\u7684\u80fd\u529b<\/td>\n\u6392\u5e8f\u6a21\u578b\u79bb\u7ebf\u8bc4\u4f30<\/td>\n<\/tr>\n
NDCG@K<\/td>\n\u8003\u8651\u6392\u5e8f\u4f4d\u7f6e\u7684\u76f8\u5173\u6027\u6307\u6807<\/td>\n\u63a8\u8350\u5217\u8868\u8d28\u91cf\u8bc4\u4f30<\/td>\n<\/tr>\n
Hit Rate@K<\/td>\n\u524dK\u4e2a\u63a8\u8350\u4e2d\u547d\u4e2d\u7528\u6237\u5b9e\u9645\u70b9\u51fb\u7684\u6bd4\u4f8b<\/td>\n\u53ec\u56de\u5c42\u8bc4\u4f30<\/td>\n<\/tr>\n
MRR<\/td>\n\u9996\u4e2a\u76f8\u5173\u7ed3\u679c\u7684\u6392\u540d\u5012\u6570<\/td>\n\u641c\u7d22+\u63a8\u8350\u8054\u5408\u8bc4\u4f30<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u5728\u7ebf\u8bc4\u4f30\u6307\u6807
\n\u6307\u6807 \u542b\u4e49 \u4e1a\u52a1\u4ef7\u503c<\/p>\n\n\u6307\u6807\u542b\u4e49\u4e1a\u52a1\u4ef7\u503c<\/tr>\n\n\n\n\n\n\n
\u70b9\u51fb\u7387（CTR）<\/td>\n\u63a8\u8350\u5185\u5bb9\u88ab\u70b9\u51fb\u7684\u6bd4\u4f8b<\/td>\n\u5185\u5bb9\u5438\u5f15\u529b<\/td>\n<\/tr>\n
\u5b8c\u64ad\u7387<\/td>\n\u89c6\u9891\u5185\u5bb9\u88ab\u5b8c\u6574\u89c2\u770b\u7684\u6bd4\u4f8b<\/td>\n\u5185\u5bb9\u8d28\u91cf\u6838\u5fc3\u6307\u6807<\/td>\n<\/tr>\n
\u505c\u7559\u65f6\u957f<\/td>\n\u7528\u6237\u5728Feed\u6d41\u7684\u603b\u505c\u7559\u65f6\u95f4<\/td>\n\u5e73\u53f0\u7c98\u6027<\/td>\n<\/tr>\n
\u5206\u4eab\u7387<\/td>\n\u5185\u5bb9\u88ab\u5206\u4eab\u7684\u6bd4\u4f8b<\/td>\n\u5185\u5bb9\u4f20\u64ad\u4ef7\u503c<\/td>\n<\/tr>\n
\u8d1f\u53cd\u9988\u7387<\/td>\n"\u4e0d\u611f\u5174\u8da3"\u70b9\u51fb\u6bd4\u4f8b<\/td>\n\u63a8\u8350\u7cbe\u51c6\u5ea6\u53cd\u9762\u6307\u6807<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4e03\u3001\u5e38\u89c1\u95ee\u9898\u4e0e\u6280\u672f\u6311\u6218<\/p>\n

\u51b7\u542f\u52a8\u95ee\u9898<\/p>\n

\u65b0\u7528\u6237\u6216\u65b0\u5185\u5bb9\u7f3a\u4e4f\u884c\u4e3a\u6570\u636e，\u4f20\u7edf\u534f\u540c\u8fc7\u6ee4\u65e0\u6cd5\u5de5\u4f5c\u3002\u89e3\u51b3\u65b9\u6848：<\/p>\n

\u4ee5\u4e0b\u4e3a\u4f2a\u4ee3\u7801，\u4ec5\u5c55\u793a\u903b\u8f91\u6d41\u7a0b<\/h2>\n

def cold_start_recommendation(new_user_features, content_pool):
\n\u201c\u201d"
\n\u51b7\u542f\u52a8\u63a8\u8350\u7b56\u7565<\/p>\n

\u57fa\u4e8e\u7528\u6237\u6ce8\u518c\u4fe1\u606f（\u5e74\u9f84\u3001\u6027\u522b\u3001\u5730\u533a）\u505a\u7c97\u7c92\u5ea6\u63a8\u8350
\n\u70ed\u95e8\u63a8\u8350\u515c\u5e95
\n\u63a2\u7d22\u6027\u63a8\u8350（Bandit\u7b97\u6cd5）
\n"""<\/p>\n

recommendations = []<\/p>\n

# \u7b56\u75651：\u4eba\u53e3\u7edf\u8ba1\u5b66\u63a8\u8350
\ndemo_based = demographic_recommendation(new_user_features)
\nrecommendations.extend(demo_based[:3])<\/p>\n

# \u7b56\u75652：\u70ed\u95e8\u63a8\u8350（\u4fdd\u8bc1\u5185\u5bb9\u8d28\u91cf\u4e0b\u9650）
\nhot_items = get_hot_items(category=new_user_features.interest_category, limit=2)
\nrecommendations.extend(hot_items)<\/p>\n

# \u7b56\u75653：Thompson Sampling\u63a2\u7d22
\nfor _ in range(5):
\n item = thompson_sampling_sample(content_pool)
\n recommendations.append(item)<\/p>\n

return recommendations<\/p>\n

\u4fe1\u606f\u8327\u623f\u4e0e\u591a\u6837\u6027<\/p>\n

\u8fc7\u5ea6\u4e2a\u6027\u5316\u4f1a\u5bfc\u81f4\u7528\u6237\u9677\u5165\u4fe1\u606f\u8327\u623f，\u89e3\u51b3\u65b9\u6848：<\/p>\n

MMR（\u6700\u5927\u8fb9\u9645\u76f8\u5173\u6027）\u91cd\u6392：\u5728\u76f8\u5173\u6027\u548c\u591a\u6837\u6027\u4e4b\u95f4\u6743\u8861
\n\u63a2\u7d22\u4e0e\u5229\u7528（E&E）\u7b56\u7565：\u5b9a\u671f\u63d2\u5165\u63a2\u7d22\u6027\u5185\u5bb9
\n\u957f\u5c3e\u5185\u5bb9\u6276\u6301：\u7ed9\u4f4e\u66dd\u5149\u9ad8\u8d28\u91cf\u5185\u5bb9\u66dd\u5149\u673a\u4f1a<\/p>\n

\u603b\u7ed3<\/p>\n

\u73b0\u4ee3\u63a8\u8350\u7cfb\u7edf\u5df2\u7ecf\u4ece\u7b80\u5355\u7684\u534f\u540c\u8fc7\u6ee4\u6f14\u8fdb\u4e3a\u878d\u5408\u6df1\u5ea6\u5b66\u4e60\u3001\u5927\u8bed\u8a00\u6a21\u578b\u3001\u751f\u6210\u5f0fAI\u7684\u590d\u6742\u5de5\u7a0b\u7cfb\u7edf\u3002\u6838\u5fc3\u67b6\u6784\u7684\u53ec\u56de-\u6392\u5e8f-\u91cd\u6392\u4e09\u6bb5\u5f0f\u8303\u5f0f\u4ecd\u7136\u662f\u5de5\u4e1a\u754c\u7684\u4e3b\u6d41\u9009\u62e9，\u4f46\u5728\u6bcf\u4e2a\u73af\u8282\u90fd\u6709\u5927\u91cf\u6280\u672f\u521b\u65b0：<\/p>\n

\u53ec\u56de\u5c42：\u4ece\u4f20\u7edfCF\u53d1\u5c55\u5230\u5411\u91cf\u53ec\u56de\u3001\u56fe\u795e\u7ecf\u7f51\u7edc\u53ec\u56de
\n\u6392\u5e8f\u5c42：\u4ece\u903b\u8f91\u56de\u5f52\u53d1\u5c55\u5230DeepFM\u3001DIN\u7b49\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b
\n\u5185\u5bb9\u7406\u89e3：\u4ece\u4eba\u5de5\u6807\u7b7e\u53d1\u5c55\u5230LLM\u81ea\u52a8\u8bed\u4e49\u7406\u89e3
\n\u63a8\u8350\u8303\u5f0f：\u4ece\u68c0\u7d22\u5f0f\u63a8\u8350\u5411\u751f\u6210\u5f0f\u63a8\u8350\u6f14\u8fdb
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