{"id":476450,"date":"2023-08-09T07:29:55","date_gmt":"2023-08-09T07:29:55","guid":{"rendered":""},"modified":"2023-09-05T11:12:45","modified_gmt":"2023-09-05T11:12:45","slug":"cosine-similarity","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/jp\/wiki\/cosine-similarity\/","title":{"rendered":"\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6"},"content":{"rendered":"<p>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306f\u3001\u6570\u5b66\u3068\u81ea\u7136\u8a00\u8a9e\u51e6\u7406 (NLP) \u306b\u304a\u3051\u308b\u57fa\u672c\u7684\u306a\u6982\u5ff5\u3067\u3042\u308a\u3001\u5185\u7a4d\u7a7a\u9593\u5185\u306e 2 \u3064\u306e\u975e\u30bc\u30ed \u30d9\u30af\u30c8\u30eb\u9593\u306e\u985e\u4f3c\u5ea6\u3092\u6e2c\u5b9a\u3057\u307e\u3059\u3002\u60c5\u5831\u691c\u7d22\u3001\u30c6\u30ad\u30b9\u30c8 \u30de\u30a4\u30cb\u30f3\u30b0\u3001\u63a8\u5968\u30b7\u30b9\u30c6\u30e0\u306a\u3069\u3001\u3055\u307e\u3056\u307e\u306a\u5206\u91ce\u3067\u5e83\u304f\u4f7f\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u6b74\u53f2\u3001\u5185\u90e8\u69cb\u9020\u3001\u7a2e\u985e\u3001\u7528\u9014\u3001\u5c06\u6765\u306e\u5c55\u671b\u306b\u3064\u3044\u3066\u8a73\u3057\u304f\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n<h2>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u8d77\u6e90\u3068\u305d\u306e\u6700\u521d\u306e\u8a00\u53ca\u306e\u6b74\u53f2<\/h2>\n<p>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u6982\u5ff5\u306f\u3001\u30b9\u30a4\u30b9\u306e\u6570\u5b66\u8005\u30a2\u30c9\u30ea\u30a2\u30f3\u30fb\u30de\u30ea\u30fc\u30fb\u30eb\u30b8\u30e3\u30f3\u30c9\u30eb\u304c\u6955\u5186\u7a4d\u5206\u306b\u95a2\u3059\u308b\u7814\u7a76\u306e\u4e00\u74b0\u3068\u3057\u3066\u5c0e\u5165\u3057\u305f 19 \u4e16\u7d00\u521d\u982d\u306b\u307e\u3067\u9061\u308a\u307e\u3059\u3002\u305d\u306e\u5f8c\u300120 \u4e16\u7d00\u306b\u306f\u3001\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306f\u3001\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3084\u30c6\u30ad\u30b9\u30c8\u306e\u985e\u4f3c\u6027\u3092\u6bd4\u8f03\u3059\u308b\u305f\u3081\u306e\u4fbf\u5229\u306a\u5c3a\u5ea6\u3068\u3057\u3066\u3001\u60c5\u5831\u691c\u7d22\u3084 NLP \u306e\u5206\u91ce\u306b\u53d6\u308a\u5165\u308c\u3089\u308c\u307e\u3057\u305f\u3002<\/p>\n<h2>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306b\u95a2\u3059\u308b\u8a73\u7d30\u60c5\u5831\u3002\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u30c8\u30d4\u30c3\u30af\u306e\u62e1\u5f35<\/h2>\n<p>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306f\u3001\u591a\u6b21\u5143\u7a7a\u9593\u3067\u6bd4\u8f03\u3059\u308b\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u307e\u305f\u306f\u30c6\u30ad\u30b9\u30c8\u3092\u8868\u3059 2 \u3064\u306e\u30d9\u30af\u30c8\u30eb\u9593\u306e\u89d2\u5ea6\u306e\u30b3\u30b5\u30a4\u30f3\u3092\u8a08\u7b97\u3057\u307e\u3059\u30022 \u3064\u306e\u30d9\u30af\u30c8\u30eb A \u3068 B \u9593\u306e\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u3092\u8a08\u7b97\u3059\u308b\u5f0f\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/p>\n<pre><div class=\"bg-black rounded-md mb-4\"><div class=\"flex items-center relative text-gray-200 bg-gray-800 px-4 py-2 text-xs font-sans justify-between rounded-t-md\"><span>css<\/span><button class=\"flex ml-auto gap-2\"><svg stroke=\"currentColor\" fill=\"none\" stroke-width=\"2\" viewbox=\"0 0 24 24\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"h-4 w-4\" height=\"1em\" width=\"1em\" ><path d=\"M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2\"><\/path><rect x=\"8\" y=\"2\" width=\"8\" height=\"4\" rx=\"1\" ry=\"1\"><\/rect><\/svg>\u30b3\u30fc\u30c9\u3092\u30b3\u30d4\u30fc\u3059\u308b<\/button><\/div><div class=\"p-4 overflow-y-auto\"><code class=\"!whitespace-pre hljs language-css\" data-no-translation=\"\">Cosine Similarity(<span class=\"hljs-selector-tag\">A<\/span>, <span class=\"hljs-selector-tag\">B<\/span>) = (<span class=\"hljs-selector-tag\">A<\/span> \u00b7 <span class=\"hljs-selector-tag\">B<\/span>) \/ (||<span class=\"hljs-selector-tag\">A<\/span>|| * ||<span class=\"hljs-selector-tag\">B<\/span>||)\n<\/code><\/div><\/div><\/pre>\n<p>\u3069\u3053 <code data-no-translation=\"\">(A \u00b7 B)<\/code> \u306f\u30d9\u30af\u30c8\u30ebA\u3068B\u306e\u30c9\u30c3\u30c8\u7a4d\u3092\u8868\u3057\u3001 <code data-no-translation=\"\">||A||<\/code> \u305d\u3057\u3066 <code data-no-translation=\"\">||B||<\/code> \u305d\u308c\u305e\u308c\u30d9\u30af\u30c8\u30eb A \u3068 B \u306e\u5927\u304d\u3055\uff08\u307e\u305f\u306f\u30ce\u30eb\u30e0\uff09\u3067\u3059\u3002<\/p>\n<p>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u7bc4\u56f2\u306f -1 \u304b\u3089 1 \u3067\u3001-1 \u306f\u5b8c\u5168\u306a\u76f8\u9055\u3092\u793a\u3057\u30011 \u306f\u7d76\u5bfe\u7684\u306a\u985e\u4f3c\u3092\u793a\u3057\u30010 \u306f\u76f4\u4ea4\u6027 (\u985e\u4f3c\u6027\u306a\u3057) \u3092\u793a\u3057\u307e\u3059\u3002<\/p>\n<h2>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u5185\u90e8\u69cb\u9020\u3002\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u4ed5\u7d44\u307f<\/h2>\n<p>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306f\u3001\u30c6\u30ad\u30b9\u30c8 \u30c7\u30fc\u30bf\u3092\u9ad8\u6b21\u5143\u7a7a\u9593\u306e\u6570\u5024\u8868\u73fe (\u30d9\u30af\u30c8\u30eb) \u306b\u5909\u63db\u3059\u308b\u3053\u3068\u3067\u6a5f\u80fd\u3057\u307e\u3059\u3002\u5404\u6b21\u5143\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u4e00\u610f\u306e\u7528\u8a9e\u306b\u5bfe\u5fdc\u3057\u307e\u3059\u30022 \u3064\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u9593\u306e\u985e\u4f3c\u5ea6\u306f\u3001\u5bfe\u5fdc\u3059\u308b\u30d9\u30af\u30c8\u30eb\u9593\u306e\u89d2\u5ea6\u306b\u57fa\u3065\u3044\u3066\u6c7a\u5b9a\u3055\u308c\u307e\u3059\u3002<\/p>\n<p>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u3092\u8a08\u7b97\u3059\u308b\u30d7\u30ed\u30bb\u30b9\u306b\u306f\u3001\u6b21\u306e\u624b\u9806\u304c\u542b\u307e\u308c\u307e\u3059\u3002<\/p>\n<ol>\n<li>\u30c6\u30ad\u30b9\u30c8\u524d\u51e6\u7406: \u30b9\u30c8\u30c3\u30d7\u30ef\u30fc\u30c9\u3084\u7279\u6b8a\u6587\u5b57\u3092\u524a\u9664\u3057\u3001\u30b9\u30c6\u30df\u30f3\u30b0\u3084\u30ec\u30de\u30bf\u30a4\u30ba\u3092\u5b9f\u884c\u3057\u3066\u30c6\u30ad\u30b9\u30c8\u3092\u6a19\u6e96\u5316\u3057\u307e\u3059\u3002<\/li>\n<li>\u7528\u8a9e\u983b\u5ea6 (TF) \u8a08\u7b97: \u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u5185\u306e\u5404\u7528\u8a9e\u306e\u983b\u5ea6\u3092\u30ab\u30a6\u30f3\u30c8\u3057\u307e\u3059\u3002<\/li>\n<li>\u9006\u6587\u66f8\u983b\u5ea6 (IDF) \u8a08\u7b97: \u3059\u3079\u3066\u306e\u6587\u66f8\u306b\u308f\u305f\u3063\u3066\u5404\u7528\u8a9e\u306e\u91cd\u8981\u5ea6\u3092\u6e2c\u5b9a\u3057\u3001\u307e\u308c\u306a\u7528\u8a9e\u306b\u9ad8\u3044\u91cd\u307f\u3092\u4ed8\u3051\u307e\u3059\u3002<\/li>\n<li>TF-IDF \u8a08\u7b97: TF \u3068 IDF \u3092\u7d44\u307f\u5408\u308f\u305b\u3066\u3001\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306e\u6700\u7d42\u7684\u306a\u6570\u5024\u8868\u73fe\u3092\u53d6\u5f97\u3057\u307e\u3059\u3002<\/li>\n<li>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u8a08\u7b97: \u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306e TF-IDF \u30d9\u30af\u30c8\u30eb\u3092\u4f7f\u7528\u3057\u3066\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u3092\u8a08\u7b97\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n<h2>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u4e3b\u306a\u7279\u5fb4\u306e\u5206\u6790<\/h2>\n<p>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306b\u306f\u3001\u30c6\u30ad\u30b9\u30c8\u6bd4\u8f03\u30bf\u30b9\u30af\u3067\u3088\u304f\u9078\u629e\u3055\u308c\u308b\u3044\u304f\u3064\u304b\u306e\u91cd\u8981\u306a\u6a5f\u80fd\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<ol>\n<li><strong>\u30b9\u30b1\u30fc\u30eb\u4e0d\u5909<\/strong>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306f\u30d9\u30af\u30c8\u30eb\u306e\u5927\u304d\u3055\u306e\u5f71\u97ff\u3092\u53d7\u3051\u306a\u3044\u305f\u3081\u3001\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306e\u9577\u3055\u306e\u5909\u5316\u306b\u5bfe\u3057\u3066\u5805\u7262\u3067\u3059\u3002<\/li>\n<li><strong>\u52b9\u7387<\/strong>: \u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u8a08\u7b97\u306f\u3001\u5927\u898f\u6a21\u306a\u30c6\u30ad\u30b9\u30c8 \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5834\u5408\u3067\u3082\u8a08\u7b97\u52b9\u7387\u304c\u9ad8\u304f\u306a\u308a\u307e\u3059\u3002<\/li>\n<li><strong>\u89e3\u91c8\u53ef\u80fd\u6027<\/strong>\u985e\u4f3c\u5ea6\u30b9\u30b3\u30a2\u306e\u7bc4\u56f2\u306f -1 \u304b\u3089 1 \u307e\u3067\u3067\u3001\u76f4\u611f\u7684\u306a\u89e3\u91c8\u304c\u53ef\u80fd\u3067\u3059\u3002<\/li>\n<li><strong>\u30c6\u30ad\u30b9\u30c8\u306e\u610f\u5473\u7684\u985e\u4f3c\u6027<\/strong>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306f\u30c6\u30ad\u30b9\u30c8\u9593\u306e\u610f\u5473\u7684\u985e\u4f3c\u6027\u3092\u8003\u616e\u3059\u308b\u305f\u3081\u3001\u30b3\u30f3\u30c6\u30f3\u30c4\u30d9\u30fc\u30b9\u306e\u63a8\u5968\u3084\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306b\u9069\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n<\/ol>\n<h2>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u7a2e\u985e<\/h2>\n<p>\u4e00\u822c\u7684\u306b\u4f7f\u7528\u3055\u308c\u308b\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306b\u306f\u3001\u4e3b\u306b 2 \u3064\u306e\u30bf\u30a4\u30d7\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<ol>\n<li><strong>\u53e4\u5178\u7684\u306a\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6<\/strong>: \u3053\u308c\u306f\u3001\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306e TF-IDF \u8868\u73fe\u3092\u4f7f\u7528\u3057\u305f\u3001\u524d\u8ff0\u3057\u305f\u6a19\u6e96\u7684\u306a\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u3067\u3059\u3002<\/li>\n<li><strong>\u30d0\u30a4\u30ca\u30ea\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6<\/strong>\u3053\u306e\u30d0\u30ea\u30a2\u30f3\u30c8\u3067\u306f\u3001\u30d9\u30af\u30c8\u30eb\u306f\u30d0\u30a4\u30ca\u30ea\u3067\u3042\u308a\u3001\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u5185\u306e\u7528\u8a9e\u306e\u5b58\u5728 (1) \u307e\u305f\u306f\u4e0d\u5728 (0) \u3092\u793a\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n<p>\u4ee5\u4e0b\u306b 2 \u3064\u306e\u30bf\u30a4\u30d7\u306e\u6bd4\u8f03\u8868\u3092\u793a\u3057\u307e\u3059\u3002<\/p>\n<table>\n<thead>\n<tr>\n<th><\/th>\n<th>\u53e4\u5178\u7684\u306a\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6<\/th>\n<th>\u30d0\u30a4\u30ca\u30ea\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u30d9\u30af\u30c8\u30eb\u8868\u73fe<\/td>\n<td>TF-IDF<\/td>\n<td>\u30d0\u30a4\u30ca\u30ea<\/td>\n<\/tr>\n<tr>\n<td>\u89e3\u91c8\u53ef\u80fd\u6027<\/td>\n<td>\u5b9f\u6570\u5024\uff08-1 \uff5e 1\uff09<\/td>\n<td>\u30d0\u30a4\u30ca\u30ea\uff080\u307e\u305f\u306f1\uff09<\/td>\n<\/tr>\n<tr>\n<td>\u306b\u9069\u3057<\/td>\n<td>\u30c6\u30ad\u30b9\u30c8\u30d9\u30fc\u30b9\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3<\/td>\n<td>\u30b9\u30d1\u30fc\u30b9\u30c7\u30fc\u30bf\u30b7\u30ca\u30ea\u30aa<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u4f7f\u3044\u65b9\u3001\u4f7f\u7528\u306b\u95a2\u3059\u308b\u554f\u984c\u3068\u305d\u306e\u89e3\u6c7a\u7b56<\/h2>\n<p>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306f\u3055\u307e\u3056\u307e\u306a\u5206\u91ce\u3067\u5fdc\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<ol>\n<li><strong>\u60c5\u5831\u691c\u7d22<\/strong>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306f\u3001\u30af\u30a8\u30ea\u3068\u306e\u95a2\u9023\u6027\u306b\u57fa\u3065\u3044\u3066\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3092\u30e9\u30f3\u30af\u4ed8\u3051\u3059\u308b\u306e\u306b\u5f79\u7acb\u3061\u3001\u52b9\u7387\u7684\u306a\u691c\u7d22\u30a8\u30f3\u30b8\u30f3\u3092\u5b9f\u73fe\u3057\u307e\u3059\u3002<\/li>\n<li><strong>\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306e\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0<\/strong>: \u985e\u4f3c\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3092\u30b0\u30eb\u30fc\u30d7\u5316\u3057\u3066\u3001\u6574\u7406\u3068\u5206\u6790\u3092\u5bb9\u6613\u306b\u3057\u307e\u3059\u3002<\/li>\n<li><strong>\u5354\u8abf\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0<\/strong>: \u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3 \u30b7\u30b9\u30c6\u30e0\u306f\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u3092\u4f7f\u7528\u3057\u3066\u3001\u4f3c\u305f\u8da3\u5473\u3092\u6301\u3064\u30e6\u30fc\u30b6\u30fc\u306b\u30a2\u30a4\u30c6\u30e0\u3092\u63d0\u6848\u3057\u307e\u3059\u3002<\/li>\n<li><strong>\u76d7\u4f5c\u691c\u51fa<\/strong>: \u7570\u306a\u308b\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u5185\u306e\u985e\u4f3c\u3057\u305f\u30c6\u30ad\u30b9\u30c8\u30bb\u30b0\u30e1\u30f3\u30c8\u3092\u8b58\u5225\u3067\u304d\u307e\u3059\u3002<\/li>\n<\/ol>\n<p>\u305f\u3060\u3057\u3001\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306f\u6b21\u306e\u3088\u3046\u306a\u5834\u5408\u306b\u8ab2\u984c\u306b\u76f4\u9762\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<ul>\n<li><strong>\u30b9\u30d1\u30fc\u30b7\u30c6\u30a3<\/strong>\u9ad8\u6b21\u5143\u306e\u30b9\u30d1\u30fc\u30b9 \u30c7\u30fc\u30bf\u3092\u6271\u3046\u5834\u5408\u3001\u985e\u4f3c\u5ea6\u30b9\u30b3\u30a2\u306f\u3042\u307e\u308a\u5f79\u306b\u7acb\u305f\u306a\u3044\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/li>\n<li><strong>\u8a00\u8a9e\u4f9d\u5b58<\/strong>: \u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u3067\u306f\u3001\u6587\u6cd5\u3084\u8a9e\u9806\u304c\u8907\u96d1\u306a\u8a00\u8a9e\u306e\u6587\u8108\u3092\u6349\u3048\u3089\u308c\u306a\u3044\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/li>\n<\/ul>\n<p>\u3053\u308c\u3089\u306e\u554f\u984c\u3092\u514b\u670d\u3059\u308b\u305f\u3081\u306b\u3001\u6b21\u5143\u524a\u6e1b (\u7279\u7570\u5024\u5206\u89e3\u306e\u4f7f\u7528\u306a\u3069) \u3084\u5358\u8a9e\u57cb\u3081\u8fbc\u307f (Word2Vec \u306a\u3069) \u306a\u3069\u306e\u624b\u6cd5\u3092\u4f7f\u7528\u3057\u3066\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u5411\u4e0a\u3055\u305b\u307e\u3059\u3002<\/p>\n<h2>\u4e3b\u306a\u7279\u5fb4\u3068\u985e\u4f3c\u7528\u8a9e\u3068\u306e\u6bd4\u8f03<\/h2>\n<table>\n<thead>\n<tr>\n<th><\/th>\n<th>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6<\/th>\n<th>\u30b8\u30e3\u30ab\u30fc\u30c9\u985e\u4f3c\u6027<\/th>\n<th>\u30e6\u30fc\u30af\u30ea\u30c3\u30c9\u8ddd\u96e2<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u6e2c\u5b9a\u30bf\u30a4\u30d7<\/td>\n<td>\u985e\u4f3c\u6027<\/td>\n<td>\u985e\u4f3c\u6027<\/td>\n<td>\u4f3c\u3066\u3044\u306a\u3044\u3053\u3068<\/td>\n<\/tr>\n<tr>\n<td>\u7bc4\u56f2<\/td>\n<td>-1\u5bfe1<\/td>\n<td>0\u304b\u30891<\/td>\n<td>0\u304b\u3089\u221e<\/td>\n<\/tr>\n<tr>\n<td>\u9069\u7528\u6027<\/td>\n<td>\u30c6\u30ad\u30b9\u30c8\u6bd4\u8f03<\/td>\n<td>\u30bb\u30c3\u30c8\u6bd4\u8f03<\/td>\n<td>\u6570\u5024\u30d9\u30af\u30c8\u30eb<\/td>\n<\/tr>\n<tr>\n<td>\u6b21\u5143\u6027<\/td>\n<td>\u9ad8\u6b21\u5143<\/td>\n<td>\u4f4e\u6b21\u5143<\/td>\n<td>\u9ad8\u6b21\u5143<\/td>\n<\/tr>\n<tr>\n<td>\u8a08\u7b97<\/td>\n<td>\u52b9\u7387\u7684<\/td>\n<td>\u52b9\u7387\u7684<\/td>\n<td>\u8a08\u7b97\u96c6\u7d04\u578b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306b\u95a2\u3059\u308b\u5c06\u6765\u306e\u5c55\u671b\u3068\u6280\u8853<\/h2>\n<p>\u6280\u8853\u304c\u9032\u6b69\u3059\u308b\u306b\u3064\u308c\u3001\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306f\u3055\u307e\u3056\u307e\u306a\u5206\u91ce\u3067\u4fa1\u5024\u3042\u308b\u30c4\u30fc\u30eb\u3067\u3042\u308a\u7d9a\u3051\u308b\u3053\u3068\u304c\u671f\u5f85\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u3088\u308a\u5f37\u529b\u306a\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u3068\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u767b\u5834\u306b\u3088\u308a\u3001\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306f\u3001\u81a8\u5927\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u51e6\u7406\u3068\u6b63\u78ba\u306a\u63a8\u5968\u4e8b\u9805\u306e\u63d0\u4f9b\u306b\u304a\u3044\u3066\u3055\u3089\u306b\u52b9\u7387\u7684\u306b\u306a\u308a\u307e\u3059\u3002\u3055\u3089\u306b\u3001\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u3068\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306e\u7d99\u7d9a\u7684\u306a\u7814\u7a76\u306b\u3088\u308a\u3001\u30c6\u30ad\u30b9\u30c8\u8868\u73fe\u304c\u6539\u5584\u3055\u308c\u3001\u985e\u4f3c\u5ea6\u8a08\u7b97\u306e\u7cbe\u5ea6\u304c\u3055\u3089\u306b\u5411\u4e0a\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<h2>\u30d7\u30ed\u30ad\u30b7\u30b5\u30fc\u30d0\u30fc\u306e\u4f7f\u7528\u65b9\u6cd5\u3084\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u3068\u306e\u95a2\u9023\u4ed8\u3051\u65b9\u6cd5<\/h2>\n<p>OneProxy \u304c\u63d0\u4f9b\u3059\u308b\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u3001\u533f\u540d\u304b\u3064\u5b89\u5168\u306a\u30a4\u30f3\u30bf\u30fc\u30cd\u30c3\u30c8 \u30a2\u30af\u30bb\u30b9\u3092\u5b9f\u73fe\u3059\u308b\u4e0a\u3067\u91cd\u8981\u306a\u5f79\u5272\u3092\u679c\u305f\u3057\u307e\u3059\u3002\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u3092\u76f4\u63a5\u5229\u7528\u3057\u306a\u3044\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u304c\u3001\u30c6\u30ad\u30b9\u30c8\u6bd4\u8f03\u3084\u30b3\u30f3\u30c6\u30f3\u30c4 \u30d9\u30fc\u30b9\u306e\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u3092\u4f7f\u7528\u3059\u308b\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306b\u95a2\u4e0e\u3067\u304d\u307e\u3059\u3002\u305f\u3068\u3048\u3070\u3001\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u3092\u5229\u7528\u3057\u3066\u30e6\u30fc\u30b6\u30fc\u306e\u597d\u307f\u3092\u6bd4\u8f03\u3057\u3001\u95a2\u9023\u3059\u308b\u30b3\u30f3\u30c6\u30f3\u30c4\u3092\u63d0\u6848\u3059\u308b\u3053\u3068\u3067\u3001\u63a8\u5968\u30b7\u30b9\u30c6\u30e0\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u5411\u4e0a\u3055\u305b\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3055\u3089\u306b\u3001\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u60c5\u5831\u691c\u7d22\u30bf\u30b9\u30af\u3092\u652f\u63f4\u3057\u3001\u30e6\u30fc\u30b6\u30fc \u30af\u30a8\u30ea\u3068\u30a4\u30f3\u30c7\u30c3\u30af\u30b9 \u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u9593\u306e\u985e\u4f3c\u5ea6\u30b9\u30b3\u30a2\u306b\u57fa\u3065\u3044\u3066\u691c\u7d22\u7d50\u679c\u3092\u6700\u9069\u5316\u3067\u304d\u307e\u3059\u3002<\/p>\n<h2>\u95a2\u9023\u30ea\u30f3\u30af<\/h2>\n<p>\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u8a73\u7d30\u306b\u3064\u3044\u3066\u306f\u3001\u6b21\u306e\u30ea\u30bd\u30fc\u30b9\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<ol>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Cosine_similarity\" target=\"_new\" rel=\"noopener nofollow\">Wikipedia \u2013 \u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6<\/a><\/li>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.pairwise.cosine_similarity.html\" target=\"_new\" rel=\"noopener nofollow\">Scikit-learn \u2013 \u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6<\/a><\/li>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.feature_extraction.text.TfidfVectorizer.html\" target=\"_new\" rel=\"noopener nofollow\">TfidfVectorizer \u2013 Sklearn \u30c9\u30ad\u30e5\u30e1\u30f3\u30c8<\/a><\/li>\n<li><a href=\"https:\/\/nlp.stanford.edu\/IR-book\/\" target=\"_new\" rel=\"noopener nofollow\">\u60c5\u5831\u691c\u7d22\u5165\u9580 \u2013 \u30de\u30cb\u30f3\u30b0\u3001\u30e9\u30ac\u30f4\u30a1\u30f3\u3001\u30b7\u30e5\u30c3\u30c4\u30a7<\/a><\/li>\n<\/ol>\n<p>\u7d50\u8ad6\u3068\u3057\u3066\u3001\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306f\u3001NLP\u3001\u60c5\u5831\u691c\u7d22\u3001\u63a8\u5968\u30b7\u30b9\u30c6\u30e0\u306a\u3069\u3001\u5e45\u5e83\u3044\u7528\u9014\u3092\u6301\u3064\u5f37\u529b\u306a\u6570\u5b66\u7684\u6982\u5ff5\u3067\u3059\u3002\u305d\u306e\u30b7\u30f3\u30d7\u30eb\u3055\u3001\u52b9\u7387\u6027\u3001\u89e3\u91c8\u306e\u3057\u3084\u3059\u3055\u304b\u3089\u3001\u3055\u307e\u3056\u307e\u306a\u30c6\u30ad\u30b9\u30c8\u30d9\u30fc\u30b9\u306e\u30bf\u30b9\u30af\u3067\u3088\u304f\u4f7f\u7528\u3055\u308c\u3001\u6280\u8853\u306e\u7d99\u7d9a\u7684\u306a\u9032\u6b69\u306b\u3088\u308a\u3001\u5c06\u6765\u7684\u306b\u305d\u306e\u6a5f\u80fd\u304c\u3055\u3089\u306b\u5f37\u5316\u3055\u308c\u308b\u3053\u3068\u304c\u671f\u5f85\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u4f01\u696d\u3084\u7814\u7a76\u8005\u304c\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u53ef\u80fd\u6027\u3092\u6d3b\u7528\u3057\u7d9a\u3051\u308b\u306b\u3064\u308c\u3066\u3001OneProxy \u306e\u3088\u3046\u306a\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u3001\u5b89\u5168\u3067\u533f\u540d\u306e\u30a4\u30f3\u30bf\u30fc\u30cd\u30c3\u30c8 \u30a2\u30af\u30bb\u30b9\u3092\u78ba\u4fdd\u3057\u306a\u304c\u3089\u3001\u3053\u308c\u3089\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u30b5\u30dd\u30fc\u30c8\u3059\u308b\u4e0a\u3067\u91cd\u8981\u306a\u5f79\u5272\u3092\u679c\u305f\u3059\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>","protected":false},"featured_media":468030,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476450","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Cosine Similarity: A Comprehensive Guide<\/mark>","faq_items":[{"question":"What is Cosine similarity?","answer":"<p>Cosine similarity is a mathematical concept used to measure the similarity between two vectors in a multi-dimensional space. It is commonly applied in text analysis, recommendation systems, and information retrieval tasks.<\/p>"},{"question":"How does Cosine similarity work?","answer":"<p>Cosine similarity calculates the cosine of the angle between two vectors, representing the documents being compared. It ranges from -1 to 1, where -1 indicates complete dissimilarity, 1 indicates absolute similarity, and 0 indicates orthogonality (no similarity).<\/p>"},{"question":"What are the key features of Cosine similarity?","answer":"<p>Cosine similarity offers scale invariance, efficiency, interpretability, and the ability to measure textual semantic similarity.<\/p>"},{"question":"What types of Cosine similarity exist?","answer":"<p>There are two primary types: Classic Cosine Similarity, which uses TF-IDF representation, and Binary Cosine Similarity, which utilizes binary vectors.<\/p>"},{"question":"How can Cosine similarity be used?","answer":"<p>Cosine similarity finds applications in various fields, including information retrieval, document clustering, collaborative filtering, and plagiarism detection.<\/p>"},{"question":"What challenges does Cosine similarity face?","answer":"<p>Cosine similarity may encounter issues with sparsity and language dependence in certain scenarios. Techniques like dimensionality reduction and word embeddings can address these challenges.<\/p>"},{"question":"How does Cosine similarity compare to other similarity measures?","answer":"<p>Cosine similarity is distinct from Jaccard similarity and Euclidean distance in terms of range, applicability, dimensionality, and computation.<\/p>"},{"question":"What are the future perspectives of Cosine similarity?","answer":"<p>As technology advances, Cosine similarity is expected to remain a valuable tool with enhanced efficiency and accuracy in similarity calculations.<\/p>"},{"question":"How are proxy servers associated with Cosine similarity?","answer":"<p>While proxy servers like OneProxy don't directly utilize Cosine similarity, they can support applications that involve text comparison and content-based filtering, such as recommendation systems and information retrieval tasks. They also ensure secure internet access during these operations.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/476450","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/476450\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media\/468030"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media?parent=476450"}],"curies":[{"name":"\u3046\u30fc\u3093","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}