{"id":61345,"date":"2026-01-17T13:17:46","date_gmt":"2026-01-17T05:17:46","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/61345.html"},"modified":"2026-01-17T13:17:46","modified_gmt":"2026-01-17T05:17:46","slug":"%e5%9f%ba%e4%ba%8e-pytorch-ncf-%e7%9a%84%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e4%b8%aa%e6%80%a7%e5%8c%96%e7%94%b5%e5%bd%b1%e6%8e%a8%e8%8d%90%e7%b3%bb%e7%bb%9f","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/61345.html","title":{"rendered":"\u57fa\u4e8e PyTorch + NCF \u7684\u6df1\u5ea6\u5b66\u4e60\u4e2a\u6027\u5316\u7535\u5f71\u63a8\u8350\u7cfb\u7edf"},"content":{"rendered":"<h3>&#x1f3ac; 1. \u7cfb\u7edf\u6548\u679c\u5c55\u793a<\/h3>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"922\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260117051743-696b1b77c3bd6.png\" width=\"1920\" \/><\/p>\n<h4>\u6838\u5fc3\u529f\u80fd\u4eae\u70b9<\/h4>\n<li>\n<p>\u6df1\u5ea6\u5b66\u4e60\u5185\u6838&#xff1a;\u6452\u5f03\u4f20\u7edf\u7684\u77e9\u9635\u5206\u89e3&#xff0c;\u91c7\u7528 PyTorch \u5b9e\u73b0\u4f55\u5411\u5357\u6559\u6388\u7ecf\u5178\u7684 NCF (Neural Collaborative Filtering) \u67b6\u6784&#xff0c;\u5229\u7528 Embedding &#043; MLP \u6355\u6349\u7528\u6237\u590d\u6742\u7684\u975e\u7ebf\u6027\u504f\u597d\u3002<\/p>\n<\/li>\n<li>\n<p>\u7528\u6237\u6027\u683c\u753b\u50cf (User Profiling)&#xff1a;\u5229\u7528 Plotly \u7ed8\u5236\u52a8\u6001\u96f7\u8fbe\u56fe&#xff0c;\u76f4\u89c2\u5c55\u793a\u7528\u6237\u7684\u89c2\u5f71\u504f\u597d&#xff08;\u5982\u201c\u70ed\u8840\/\u5192\u9669\u201d\u3001\u201c\u611f\u6027\/\u6d6a\u6f2b\u201d\u7b49&#xff09;\u3002<\/p>\n<\/li>\n<li>\n<p>\u89e3\u51b3\u51b7\u542f\u52a8 (Cold Start)&#xff1a;\u72ec\u521b \u201c\u6570\u5b57\u5b6a\u751f (Digital Twin)\u201d \u5339\u914d\u7b97\u6cd5\u3002\u65b0\u7528\u6237\u53ea\u9700\u9009\u62e9\u51e0\u4e2a\u6807\u7b7e&#xff0c;\u7cfb\u7edf\u5373\u53ef\u5728\u767e\u4e07\u7ea7\u5386\u53f2\u6570\u636e\u4e2d\u627e\u5230\u6700\u76f8\u4f3c\u7684\u201c\u66ff\u8eab\u7528\u6237\u201d&#xff0c;\u501f\u7528\u5176\u7279\u5f81\u5411\u91cf\u8fdb\u884c\u7cbe\u51c6\u63a8\u8350\u3002<\/p>\n<\/li>\n<li>\n<p>\u591a\u6837\u6027\u7b56\u7565&#xff1a;\u5f15\u5165 Candidate Pool Sampling \u673a\u5236&#xff0c;\u62d2\u7edd\u5343\u7bc7\u4e00\u5f8b\u7684\u63a8\u8350\u7ed3\u679c&#xff0c;\u4fdd\u8bc1\u6bcf\u6b21\u5237\u65b0\u90fd\u6709\u60ca\u559c (Serendipity)\u3002<\/p>\n<\/li>\n<hr \/>\n<h3>&#x1f6e0;\ufe0f 2. \u6280\u672f\u6808\u4e0e\u67b6\u6784<\/h3>\n<ul>\n<li>\n<p>\u5f00\u53d1\u8bed\u8a00&#xff1a;Python 3.10<\/p>\n<\/li>\n<li>\n<p>\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6&#xff1a;PyTorch (GPU\/CPU \u81ea\u52a8\u9002\u914d)<\/p>\n<\/li>\n<li>\n<p>\u524d\u7aef\u53ef\u89c6\u5316&#xff1a;Streamlit &#043; Plotly &#043; Pandas<\/p>\n<\/li>\n<li>\n<p>\u6570\u636e\u96c6&#xff1a;MovieLens (ml-latest) \u5343\u4e07\u7ea7\u6570\u636e\u6e05\u6d17<\/p>\n<\/li>\n<\/ul>\n<h4>&#x1f9e0; NCF \u6a21\u578b\u67b6\u6784\u56fe<\/h4>\n<p>\u672c\u9879\u76ee\u6a21\u578b\u7ed3\u6784\u5982\u4e0b&#xff1a;<\/p>\n<ul>\n<li>\n<p>Input Layer: \u63a5\u6536 User ID \u548c Movie ID\u3002<\/p>\n<\/li>\n<li>\n<p>Embedding Layer: \u5c06\u7a00\u758f ID \u6620\u5c04\u4e3a 32\u7ef4 \u7a20\u5bc6\u5411\u91cf\u3002<\/p>\n<\/li>\n<li>\n<p>Interaction Layer: \u4f7f\u7528\u591a\u5c42\u611f\u77e5\u673a (MLP) \u66ff\u4ee3\u70b9\u79ef&#xff0c;\u63d0\u53d6\u9ad8\u9636\u7279\u5f81\u4ea4\u4e92\u3002<\/p>\n<\/li>\n<li>\n<p>Output Layer: \u8f93\u51fa\u9884\u6d4b\u8bc4\u5206 (Rating Prediction)\u3002<\/p>\n<\/li>\n<\/ul>\n<h3>&#x1f4c2; 3. \u9879\u76ee\u5de5\u7a0b\u7ed3\u6784<\/h3>\n<p>\u672c\u9879\u76ee\u91c7\u7528\u6a21\u5757\u5316 (Modular) \u8bbe\u8ba1&#xff0c;\u7b26\u5408\u8f6f\u4ef6\u5de5\u7a0b\u89c4\u8303&#xff0c;\u89e3\u8026\u6e05\u6670&#xff1a;<\/p>\n<p>DeepRec_System\/<br \/>\n\u251c\u2500\u2500 result\/ \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # [\u4ea7\u7269] \u5b58\u653e\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u4e0e\u5143\u6570\u636e<br \/>\n\u2502 \u00a0 \u251c\u2500\u2500 ncf_model.pth \u00a0 \u00a0 \u00a0 # PyTorch \u6a21\u578b\u6743\u91cd<br \/>\n\u2502 \u00a0 \u251c\u2500\u2500 meta_data.pkl \u00a0 \u00a0 \u00a0 # ID \u6620\u5c04\u5b57\u5178<br \/>\n\u2502 \u00a0 \u2514\u2500\u2500 loss_curve.png \u00a0 \u00a0 \u00a0# \u8bad\u7ec3 Loss \u66f2\u7ebf\u56fe<br \/>\n\u251c\u2500\u2500 movies.csv \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# \u7535\u5f71\u5143\u6570\u636e (\u6807\u9898\u3001\u6d41\u6d3e)<br \/>\n\u251c\u2500\u2500 model_def.py \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# [\u6838\u5fc3] NCF \u795e\u7ecf\u7f51\u7edc\u5b9a\u4e49<br \/>\n\u251c\u2500\u2500 backend.py \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# [\u4e1a\u52a1] \u63a8\u8350\u903b\u8f91\u3001\u6570\u5b57\u5b6a\u751f\u7b97\u6cd5\u3001\u753b\u50cf\u751f\u6210<br \/>\n\u251c\u2500\u2500 app.py \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# [\u524d\u7aef] Streamlit \u4ea4\u4e92\u754c\u9762<br \/>\n\u2514\u2500\u2500 train.py \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# [\u8bad\u7ec3] \u6a21\u578b\u8bad\u7ec3\u811a\u672c (\u652f\u6301\u670d\u52a1\u5668\u591a\u5361\u8bad\u7ec3)<\/p>\n<h3>&#x1f680; 4. \u6838\u5fc3\u529f\u80fd\u8be6\u7ec6\u6f14\u793a<\/h3>\n<h4>4.1 \u201c\u6570\u5b57\u5b6a\u751f\u201d\u51b7\u542f\u52a8\u63a8\u8350<\/h4>\n<p>\u9488\u5bf9\u6ca1\u6709\u5386\u53f2\u8bb0\u5f55\u7684\u65b0\u7528\u6237&#xff0c;\u7cfb\u7edf\u63d0\u4f9b\u201c\u81ea\u5b9a\u4e49\u753b\u50cf\u201d\u529f\u80fd\u3002<\/p>\n<li>\n<p>\u7528\u6237\u8f93\u5165&#xff1a;\u201c\u6211\u559c\u6b22\u79d1\u5e7b\u3001\u52a8\u4f5c&#xff0c;\u4e14\u53ea\u770b 2015 \u5e74\u540e\u7684\u7535\u5f71\u201d\u3002<\/p>\n<\/li>\n<li>\n<p>\u540e\u53f0\u7b97\u6cd5&#xff1a;\u5168\u5e93\u68c0\u7d22&#xff0c;\u627e\u5230\u4e00\u4f4d\u6253\u5206\u884c\u4e3a\u9ad8\u5ea6\u4e00\u81f4\u7684\u8d44\u6df1\u8001\u7528\u6237&#xff08;\u6570\u5b57\u66ff\u8eab&#xff09;\u3002<\/p>\n<\/li>\n<li>\n<p>\u5411\u91cf\u8fc1\u79fb&#xff1a;\u501f\u7528\u8be5\u8001\u7528\u6237\u7684 Embedding \u5411\u91cf\u8f93\u5165 NCF \u6a21\u578b\u3002<\/p>\n<\/li>\n<li>\n<p>\u7ed3\u679c&#xff1a;\u751f\u6210\u5b8c\u7f8e\u7684\u51b7\u542f\u52a8\u63a8\u8350\u5217\u8868\u3002<\/p>\n<\/li>\n<h4>4.2\u00a0\u6df7\u5408\u68c0\u7d22 (Hybrid Search &#043; 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