{"id":1436,"date":"2024-07-26T01:19:43","date_gmt":"2024-07-25T17:19:43","guid":{"rendered":"http:\/\/tobykskgd.life\/?p=1436"},"modified":"2024-11-14T22:28:21","modified_gmt":"2024-11-14T14:28:21","slug":"70","status":"publish","type":"post","link":"https:\/\/tobykskgd.life\/index.php\/70\/","title":{"rendered":"\u5434\u6069\u8fbe\u673a\u5668\u5b66\u4e60\u7b14\u8bb0EP13"},"content":{"rendered":"\n

\u3010\u63a8\u8350\u7cfb\u7edf\u76f8\u5173\u548c\u5f3a\u5316\u5b66\u4e60\u3011\u5434\u6069\u8fbe\u673a\u5668\u5b66\u4e60\u7b14\u8bb0EP13\uff08P121-P130\uff09<\/p>\n\n\n\n

\"\"<\/div><\/figure>\n\n\n\n

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\n\n\n\n

\u534f\u540c\u8fc7\u6ee4 VS \u57fa\u4e8e\u5185\u5bb9\u7684\u8fc7\u6ee4<\/h2>\n\n\n\n

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\"\"<\/div><\/figure>\n\n\n\n

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\"\"<\/div><\/figure>\n\n\n\n

Deep learning for content-based filtering<\/h2>\n\n\n\n

\u4e0b\u9762\u5c31\u4ecb\u7ecd\u4e86vu<\/sub>\u548cvm<\/sub>\u7684\u7531\u6765\uff0c\u4e3b\u8981\u662f\u8bad\u7ec3\u4e24\u4e2a\u795e\u7ecf\u7f51\u7edc\uff0c\u7136\u540e\u5206\u522b\u901a\u8fc7xu<\/sub>\u548cxm<\/sub>\u8ba1\u7b97\u6216\u8005\u8bf4\u63a8\u7406vu<\/sub>\u548cvm<\/sub>\u3002<\/p>\n\n\n\n

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\"\"<\/div><\/figure>\n\n\n\n

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\"\"<\/div><\/figure>\n\n\n\n

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\"\"<\/div><\/figure>\n\n\n\n

\u4ece\u5927\u76ee\u5f55\u4e2d\u63a8\u8350<\/h2>\n\n\n\n

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\"\"<\/div><\/figure>\n\n\n\n

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\"\"<\/div><\/figure>\n\n\n\n

\u4e24\u4e2a\u8fc7\u7a0b\u7ec6\u8282\uff1a<\/p>\n\n\n\n

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\"\"<\/div><\/figure>\n\n\n\n

\u63a8\u8350\u7cfb\u7edf\u7684\u9053\u5fb7\u4f7f\u7528<\/h2>\n\n\n\n

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\"\"<\/div><\/figure>\n\n\n\n

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\"\"<\/div><\/figure>\n\n\n\n

\u57fa\u4e8e\u5185\u5bb9\u8fc7\u6ee4\u7684TensorFlow\u5b9e\u73b0<\/h2>\n\n\n\n

\u4ee3\u7801\u4e2d\u8fdb\u884c\u8bf4\u660e\uff1a<\/p>\n\n\n\n

#\u4e24\u4e2a\u795e\u7ecf\u7f51\u7edc\u7684\u6784\u5efa\nuser_NN = tf.keras.models.Sequential([\n    tf.keras.layers.Dense(256, activation = 'relu')\n    tf.keras.layers.Dense(128, activation = 'relu')\n    tf.keras.layers.Dense(32)\n])\n\nitem_NN = tf.keras.models.Sequential([\n    tf.keras.layers.Dense(256, activation = 'relu')\n    tf.keras.layers.Dense(128, activation = 'relu')\n    tf.keras.layers.Dense(32)\n])\n\n# \u521b\u5efa\u7528\u6237\u8f93\u5165\u548c\u5206\u6570\u5230\u57fa\u7840\u7f51\u7edc\u5f53\u4e2d\ninput_user = tf.keras.layers.Input(shape=(num_user_features)) # \u4e3a\u7528\u6237\u63d0\u53d6\u8f93\u5165\u7279\u5f81\nvu = user_NN(input_user)\nvu = tf.linalg.l2_normalize(vu, axis=1) # \u5c06\u5411\u91cfvu\u5f52\u4e00\u5316\u4e3a\u957f\u5ea61\n\ninput_item = tf.keras.layers.Input(shape=(num_user_features)) # \u4e3a\u7528\u6237\u63d0\u53d6\u8f93\u5165\u7279\u5f81\nvm = item_NN(input_user)\nvm = tf.linalg.l2_normalize(vu, axis=1) # \u5c06\u5411\u91cfvm\u5f52\u4e00\u5316\u4e3a\u957f\u5ea61\n\n# \u7ed3\u5408\u4e24\u4e2a\u795e\u7ecf\u7f51\u7edc\uff0c\u505a\u70b9\u4e58\noutput = tf.keras.layers.Dot(axis=1)([vu, vm])\n\nmodel = Model([input_user, input_item], output) # \u7ed9\u6a21\u578b\u8f93\u5165\u548c\u8f93\u51fa\n\ncost_fn = tf.keras.losses.MeanSquareError() # loss\u5b9a\u4e49<\/code><\/pre>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u4ec0\u4e48\u662f\u5f3a\u5316\u5b66\u4e60<\/h2>\n\n\n\n
\u8fdb\u5165\u672c\u8bfe\u7a0b\u6700\u540e\u7684\u5185\u5bb9\u5f3a\u5316\u5b66\u4e60\u3002<\/p>\n\n\n\n
\u5f3a\u5316\u5b66\u4e60\u5c31\u662f\u544a\u8bc9\u7cfb\u7edf\u4ec0\u4e48\u662f\u5bf9\u7684\uff0c\u4ec0\u4e48\u662f\u9519\u7684\uff0c\u4f46\u662f\u4e0d\u8bf4\u600e\u4e48\u505a\uff0c\u8ba9\u7cfb\u7edf\u81ea\u5df1\u53bb\u5c1d\u8bd5\u3002<\/p>\n\n\n\n
\u9488\u5bf9\u8fd9\u79cd\u65b9\u6cd5\uff0c\u5f15\u5165\u5956\u52b1\u51fd\u6570x\u3002\u5c31\u662f\u5982\u679c\u7cfb\u7edf\u505a\u5bf9\uff0c\u5c31\u7ed9\u4e00\u70b9\u5956\u52b1\uff1b\u5982\u679c\u7cfb\u7edf\u505a\u9519\uff0c\u5c31\u7ed9\u5927\u4e00\u70b9\u7684\u60e9\u7f5a\u3002<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u706b\u661f\u63a2\u6d4b\u5668\u793a\u4f8b<\/h2>\n\n\n\n
\u4e0b\u9762\u7528\u706b\u661f\u63a2\u6d4b\u5668\u4f5c\u4e3a\u793a\u4f8b\u3002<\/p>\n\n\n\n
state\u662f\u6bcf\u4e2a\u72b6\u6001\u4e0b\u9762\u6709\u5bf9\u6620\u7684\u5956\u52b1\u3002\u6bd4\u5982\u8bf4\u706b\u661f\u63a2\u6d4b\u5668\u5728\u72b6\u60011\u548c6\u4e0a\u9762\u8fd4\u56de\u597d\u7684\u63a2\u6d4b\u7167\u7247\uff0c\u5c31\u7ed9\u4e88100\u548c40\u7684\u5956\u52b1\u3002<\/p>\n\n\n\n
\u901a\u8fc7\u5956\u52b1\u51b3\u5b9a\u5982\u4f55\u91c7\u53d6\u884c\u52a8\uff0c\u7ecf\u8fc7\u4e0d\u65ad\u8bd5\u9519\uff0c\u627e\u5230\u83b7\u53d6\u5956\u52b1\u6700\u5927\u7684\u60c5\u51b5\u3002<\/p>\n\n\n\n
\u4e0b\u9762\u662f\u4e00\u4e2a\u5199\u6cd5\u793a\u4f8b\uff1a(s, a, R(s), s’)\uff0c\u4ee3\u8868\u7684\u662f(\u76ee\u524d\u72b6\u6001\u4f4d\u7f6e\uff0c \u65b9\u5411\uff0c \u5bf9\u5e94\u5956\u52b1\uff0c \u65b0\u72b6\u6001\u4f4d\u7f6e)\u3002<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
The Return in reinforcement learning<\/h2>\n\n\n\n
\u5f3a\u5316\u5b66\u4e60\u4e2d\u518d\u5f15\u5165\u4e00\u4e2a\u8d34\u73b0\u56e0\u5b50\u7684\u6982\u5ff5\uff0c\u7528\u6765\u7efc\u5408\u8861\u91cf\u9ad8\u4ef7\u503c\u7684\u5956\u52b1\u548c\u8981\u66f4\u65b0\u7684\u6b21\u6570\uff0c\u66f4\u5feb\u83b7\u5f97\u5956\u52b1\u4f1a\u5bfc\u81f4\u603b\u56de\u62a5\u7684\u4ef7\u503c\u66f4\u9ad8\u3002<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u51b3\u7b56\u4e0e\u7b56\u7565\u5236\u5b9a<\/h2>\n\n\n\n
\u518d\u6765\u5f15\u5165\u4e00\u4e2a\u6982\u5ff5\uff0c\u03c0\u662f\u72b6\u6001\u7b56\u7565\u51fd\u6570\uff0c\u6ce8\u610f\u8fd9\u91ccpai\u662f\u4e2a\u51fd\u6570\u4e0d\u662f\u4e00\u4e2a\u5e38\u6570\uff01\u7528\u6765\u544a\u8bc9\u7cfb\u7edf\u5f53\u524d\u72b6\u6001\u4e0b\u600e\u4e48\u505a\u662f\u6700\u597d\u7684\u3002<\/p>\n\n\n\n
\u5f3a\u5316\u5b66\u4e60\u7684\u76ee\u6807\u662f\u627e\u5230\u4e00\u4e2a\u7b56\u7565\uff0c\u544a\u8bc9\u7cfb\u7edf\u6bcf\u4e2a\u72b6\u6001\u4e0b\u5e94\u8be5\u91c7\u53d6\u4ec0\u4e48\u52a8\u4f5c\u3002<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u56de\u987e\u5173\u952e\u6982\u5ff5<\/h2>\n\n\n\n
\u6700\u540e\u662f\u524d\u9762\u4ecb\u7ecd\u7684\u76f8\u5173\u6982\u5ff5\u7684\u56de\u987e\u548c\u5bf9\u5e94\u6982\u5ff5\u7684\u793a\u4f8b\u3002<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u6700\u540e\u4ecb\u7ecd\u4e86\u5f3a\u5316\u5b66\u4e60\u7684\u8fc7\u7a0b\uff0c\u4e5f\u5c31\u662f\u9a6c\u5c14\u53ef\u592b\u51b3\u7b56\u8fc7\u7a0b\u3002<\/p>\n\n\n\n
\u5206\u4e3a\u66f4\u65b0\u7b56\u7565\u03c0\uff0c\u7136\u540e\u505a\u51fa\u884c\u4e3a\uff0c\u63a5\u7740\u4e0e\u73af\u5883\/\u4e16\u754c\u8fdb\u884c\u5b9e\u9645\u7684\u4ea4\u4e92\uff0c\u6700\u540e\u66f4\u65b0\u72b6\u6001\uff0c\u770b\u662f\u5426\u7ed9\u4e88\u5956\u52b1\u3002<\/p>\n\n\n\n
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\"\"<\/div><\/figure>\n","protected":false},"excerpt":{"rendered":"
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