{"id":608,"date":"2021-03-19T19:11:22","date_gmt":"2021-03-19T10:11:22","guid":{"rendered":"https:\/\/technologyagency.net\/wordpress\/?p=608"},"modified":"2021-03-27T14:47:20","modified_gmt":"2021-03-27T05:47:20","slug":"recommendation-system%e3%82%92%e8%80%83%e3%81%88%e3%82%8b2","status":"publish","type":"post","link":"https:\/\/technologyagency.net\/wordpress\/2021\/03\/19\/recommendation-system%e3%82%92%e8%80%83%e3%81%88%e3%82%8b2\/","title":{"rendered":"Recommendation System\u3092\u8003\u3048\u308b \u305d\u306e\uff12"},"content":{"rendered":"\n

Recommendation System\u3092\u8003\u3048\u308b<\/a>\u306e\u7d9a\u304d\u3002<\/p>\n\n\n\n

\u66f4\u65b0\u5c65\u6b74\uff1a2021\/03\/19 \u30c7\u30fc\u30bf\u51e6\u7406\u306b\u8aa4\u308a\u304c\u3042\u3063\u305f\u305f\u3081\u8a02\u6b63<\/p>\n\n\n\n

\u521d\u3081\u306b<\/h2>\n\n\n\n

\u5c0f\u8aac\u3092\u30e6\u30fc\u30b6\u30fc\u306b\u63a8\u85a6\u3059\u308b\u304b\u3057\u306a\u3044\u304b\u3067\u5206\u3051\u305f\u3044\u306e\u3067\u3001\u3053\u308c\u306f\u5206\u985e\u554f\u984c\u3068\u306a\u308b\u3002\u5206\u985e\u554f\u984c\u306b\u3064\u3044\u3066\u306f\u3053\u3061\u3089<\/a>\u3067\u89e3\u8aac\u3057\u3066\u3044\u308b\u3002<\/p>\n\n\n\n

\u5206\u985e\u3059\u308b\u305f\u3081\u306b\u3001\u4f5c\u8005\u3001\u95a2\u9023\u4f5c\u54c1\u3084\u30bf\u30b0\u4ed8\u3051\u3082\u53c2\u7167\u3057\u305f\u65b9\u304c\u7cbe\u5ea6\u304c\u826f\u304f\u306a\u308b\u304b\u3082\u3057\u308c\u306a\u3044\u304c\u3001\u4eca\u56de\u306fDoc2Vec\u306e\u30d9\u30af\u30c8\u30eb\u60c5\u5831\u306e\u307f\u3067\u884c\u3063\u3066\u307f\u308b\u3002Doc2Vec \u3067\u4f5c\u6210\u3057\u305f\u6587\u66f8\u30d9\u30af\u30c8\u30eb\u3092\u6559\u5e2b\u3042\u308a\u5b66\u7fd2\u306e\u8aac\u660e\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3059\u308b\u3002<\/p>\n\n\n\n

\u6587\u66f8\u30d9\u30af\u30c8\u30eb\u3092\u6559\u5e2b\u3042\u308a\u5b66\u7fd2\u306e\u8aac\u660e\u5909\u6570\u3068\u3057\u305f\u5206\u985e\u554f\u984c\u3067\u306fRandom Forest\u304c\u6709\u52b9\u3068\u3044\u3046\u8ad6\u6587\u3092\u898b\u305f\u306e\u3067\u3001\u307e\u305a\u306f Random Forest \u3092\u8a66\u3057\u3066\u307f\u308b\u3002\u307e\u305f\u3001\u4eca\u56de\u4f7f\u7528\u3059\u308bDoc2Vec\u306e\u6587\u66f8\u30d9\u30af\u30c8\u30eb\u306f\u6b21\u5143\u6570\u304c100\u307b\u3069\u3042\u308b\u305f\u3081\u3001\u4e8b\u524d\u306b\u6b21\u5143\u5727\u7e2e\u3092\u884c\u3046\u3053\u3068\u3082\u691c\u8a0e\u3059\u308b\u3002<\/p>\n\n\n\n

\u6700\u521d\u306f\u3001\u3053\u306e\u30e2\u30c7\u30eb\u3092\u6b63\u3057\u304f\u8a55\u4fa1\u3059\u308b\u305f\u3081\u306b\u3001\u30e6\u30fc\u30b6\u30fc\u306e\u597d\u307f\u304b\u306e\u69d8\u306a\u3042\u3044\u307e\u3044\u306a\u6307\u6a19\u3067\u5206\u985e\u3059\u308b\u306e\u3067\u306f\u306a\u304f\u3001\u6a5f\u68b0\u7684\u306b\u5206\u985e\u3067\u304d\u308b\u6307\u6a19\u306b\u3059\u308b\u3002<\/p>\n\n\n\n

\u5206\u985e\u306e\u5b9f\u65bd<\/h2>\n\n\n\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u4f5c\u6210<\/h5>\n\n\n\n

\u4ee5\u524d\u4f5c\u6210\u3057\u305f\u5b66\u7fd2\u6e08\u307f\u30e2\u30c7\u30eb\u3092\u8aad\u307f\u8fbc\u307f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f5c\u6210\u3059\u308b\u3002<\/p>\n\n\n

\nfrom gensim.models.doc2vec import Doc2Vec\n\nMODEL_DATA_PATH = 'drive\/My Drive\/Colab Notebooks\/syosetu\/doc2vec_100.model'\n\nm = Doc2Vec.load(MODEL_DATA_PATH)\nvectors_list = [m.docvecs[n] for n in range(len(m.docvecs))]\n<\/pre><\/div>\n\n
\nimport pandas as pd\n\nMODEL_TITLES_CSV_PATH = 'drive\/My Drive\/Colab Notebooks\/syosetu\/novel_titles.csv'\n\ndf = pd.read_csv(MODEL_TITLES_CSV_PATH, header=None, names=['url', 'title', 'tag'])\ndf = df.drop(columns=['url'])\ndf['vectors'] = vectors_list\n\ndf.head(2)\n<\/pre><\/div>\n\n\n
<\/th>title<\/th>tag<\/th>vectors<\/th><\/tr><\/thead>
0<\/th>\u8ee2\u751f\u3057\u305f\u3089\u30b9\u30e9\u30a4\u30e0\u3060\u3063\u305f\u4ef6<\/td>R15 \u6b8b\u9177\u306a\u63cf\u5199\u3042\u308a \u7570\u4e16\u754c\u8ee2\u751f \u30b9\u30e9\u30a4\u30e0 \u30c1\u30fc\u30c8<\/td>[5.356541, 1.455931, 1.0426528, 1.1359314, 3.9…<\/td><\/tr>
1<\/th>\u7121\u8077\u8ee2\u751f\u3000- \u7570\u4e16\u754c\u884c\u3063\u305f\u3089\u672c\u6c17\u3060\u3059 –<\/td>R15 \u6b8b\u9177\u306a\u63cf\u5199\u3042\u308a \u7570\u4e16\u754c\u8ee2\u751f<\/td>[0.010144993, -12.32704, 2.8596005, 6.5284176,…<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u30bf\u30b0\u3092\u898b\u3066\u6a5f\u68b0\u7684\u306b\u5206\u985e\u3057\u305f\u3044\u305f\u3081\u3001\u3069\u3093\u306a\u30bf\u30b0\u304c\u5b58\u5728\u3059\u308b\u306e\u304b\u6700\u521d\u306b\u78ba\u8a8d\u3059\u308b\u3002<\/p>\n\n\n
\nimport collections\nimport itertools\n\ntag = collections.Counter(list(itertools.chain.from_iterable(df['tag'].str.split().tolist())))\nsorted(tag.items(), key=lambda pair: pair[1], reverse=True)[:20]\n<\/pre><\/div>\n\n
\n[('R15', 233),\n ('\u6b8b\u9177\u306a\u63cf\u5199\u3042\u308a', 215),\n ('\u7570\u4e16\u754c\u8ee2\u751f', 98),\n ('\u7570\u4e16\u754c', 87),\n ('\u7570\u4e16\u754c\u8ee2\u79fb', 86),\n ('\u30c1\u30fc\u30c8', 84),\n ('\u30d5\u30a1\u30f3\u30bf\u30b8\u30fc', 80),\n ('\u9b54\u6cd5', 73),\n ('\u7537\u4e3b\u4eba\u516c', 65),\n ('\u5192\u967a', 63),\n ('\u4e3b\u4eba\u516c\u6700\u5f37', 59),\n ('\u66f8\u7c4d\u5316', 53),\n ('\u30cf\u30fc\u30ec\u30e0', 48),\n ('\u6210\u308a\u4e0a\u304c\u308a', 43),\n ('\u307b\u306e\u307c\u306e', 33),\n ('\u30cf\u30c3\u30d4\u30fc\u30a8\u30f3\u30c9', 32),\n ('\u5973\u4e3b\u4eba\u516c', 31),\n ('\u604b\u611b', 30),\n ('\u30aa\u30ea\u30b8\u30ca\u30eb\u6226\u8a18', 30),\n ('\u8ee2\u751f', 28)]\n<\/pre><\/div>\n\n\n
2\u6975\u5316\u3057\u3084\u3059\u305d\u3046\u306a\u3001\u7537\u4e3b\u4eba\u516c\u3001\u5973\u4e3b\u4eba\u516c\u306e\u5c0f\u8aac\u3092\u62bd\u51fa\u3059\u308b\u3053\u3068\u306b\u3059\u308b\u3002<\/p>\n\n\n
\ndf_m = df[df['tag'].str.contains('\u7537\u4e3b\u4eba\u516c')]\ndf_m['female'] = 0\n<\/pre><\/div>\n\n
\ndf_f = df[df['tag'].str.contains('\u5973\u4e3b\u4eba\u516c|\u5973\u6027\u8996\u70b9|\u60aa\u5f79\u4ee4\u5b22|\u5f8c\u5bae|\u5c11\u5973\u30de\u30f3\u30ac')]\ndf_f['female'] = 1\n<\/pre><\/div>\n\n\n
\u4e0a\u8a18\u306e\u62bd\u51fa\u65b9\u6cd5\u306e\u5834\u5408\u3001\u5973\u4e3b\u4eba\u516c\u3068\u5206\u985e\u3057\u305f\u5c0f\u8aac\u306e\u4e2d\u306b\u7537\u4e3b\u4eba\u516c\u306e\u5c0f\u8aac\u304c\u6df7\u3058\u3063\u3066\u3044\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u3002<\/p>\n\n\n
\ndf_f[df_f['tag'].str.contains('\u7537\u4e3b\u4eba\u516c')]\n<\/pre><\/div>\n\n\n
<\/th>title<\/th>tag<\/th>vectors<\/th><\/tr><\/thead>
47<\/th>\u4e59\u5973\u30b2\u30fc\u4e16\u754c\u306f\u30e2\u30d6\u306b\u53b3\u3057\u3044\u4e16\u754c\u3067\u3059<\/td>R15 \u6b8b\u9177\u306a\u63cf\u5199\u3042\u308a \u7570\u4e16\u754c\u8ee2\u751f \u4e59\u5973\u30b2\u30fc\u30e0 \u60aa\u5f79\u4ee4\u5b22 \u5192\u967a \u4eba\u5de5\u77e5\u80fd \u7537\u4e3b\u4eba\u516c \u5b66\u5712 …<\/td>[6.775977, 4.3698134, -1.9109731, 9.731645, -0…<\/td><\/tr>
74<\/th>\u3010Web\u7248\u3011\u753a\u4ebaA\u306f\u60aa\u5f79\u4ee4\u5b22\u3092\u3069\u3046\u3057\u3066\u3082\u6551\u3044\u305f\u3044\u3010\u672c\u7de8\u5b8c\u7d50\u6e08\u307f\u3011<\/td>R15 \u6b8b\u9177\u306a\u63cf\u5199\u3042\u308a \u7570\u4e16\u754c\u8ee2\u751f \u4e59\u5973\u30b2\u30fc\u30e0 \u60aa\u5f79\u4ee4\u5b22 \u30aa\u30ea\u30b8\u30ca\u30eb\u6226\u8a18 \u7537\u4e3b\u4eba\u516c \u897f\u6d0b …<\/td>[2.9941628, 1.892081, 1.9338807, 4.0745897, 6….<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u4e0a\u8a18\u306f\u7537\u4e3b\u4eba\u516c\u306e\u5c0f\u8aac\u3068\u601d\u308f\u308c\u308b\u305f\u3081\u3001\u5973\u4e3b\u4eba\u516c\u306e\u5c0f\u8aac\u306e\u4e00\u89a7\u304b\u3089\u9664\u5916\u3059\u308b\u3002<\/p>\n\n\n
\ndf_f = df_f.drop([47, 74])\n<\/pre><\/div>\n\n
\ndf = pd.concat([df_m, df_f])\ndf['female'].value_counts()\n<\/pre><\/div>\n\n\n
0 67
1 51
Name: female, dtype: int64<\/p>\n\n\n\n
\u7537\u4e3b\u4eba\u516c\u306e\u5c0f\u8aac\u306e\u65b9\u304c\u6570\u304c\u591a\u3044\u305f\u3081\u3001\u5973\u4e3b\u4eba\u516c\u306e\u5c0f\u8aac\u306e\u6570\u3068\u5408\u308f\u305b\u308b\u3002<\/p>\n\n\n
\n#\u7537\u4e3b\u4eba\u516c\u306e\u4e00\u89a7\ndf_0 = df[df['female'] == 0]\n#\u5973\u4e3b\u4eba\u516c\u306e\u4e00\u89a7\ndf_1 = df[df['female'] == 1]\n\ndf_0 = df_0.sample(n=len(df_1), random_state=0)\n\ndf = pd.concat([df_0, df_1])\n<\/pre><\/div>\n\n\n
\u6a5f\u68b0\u5b66\u7fd2\u306e\u5b9f\u65bd<\/h5>\n\n\n\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u4f5c\u6210\u304c\u51fa\u6765\u305f\u306e\u3067\u3001 Random Forest \u3092\u7528\u3044\u3066\u3001\u5206\u985e\u3092\u5b9f\u65bd\u3057\u3066\u307f\u308b\u3002<\/p>\n\n\n
\nX = df['vectors']\ny = df['female']\n\nX_array = np.array([np.array(v) for v in X])\ny_array = np.array([i for i in y])\n<\/pre><\/div>\n\n
\nfrom sklearn.model_selection import train_test_split\n\nX_train, X_test, y_train, y_test = train_test_split(X_array, y_array, random_state=0)\n<\/pre><\/div>\n\n
\nfrom sklearn.ensemble import RandomForestClassifier\n\nrandom_forest = RandomForestClassifier(random_state=0)\nrandom_forest.fit(X_train, y_train)\n\nrandom_forest.score(X_test, y_test)\n<\/pre><\/div>\n\n\n
0.8076923076923077<\/p>\n\n\n
\nrandom_forest.predict(X_test)\n<\/pre><\/div>\n\n\n
array([0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 1, 0, 0])<\/p>\n\n\n\n
\u8a66\u3057\u306b\u3001\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u3067\u3082\u5b9f\u65bd\u3057\u3066\u307f\u308b\u3002<\/p>\n\n\n
\nfrom sklearn.linear_model import LogisticRegression\n\nlogreg = LogisticRegression()\nlogreg.fit(X_train, y_train)\n\nlogreg.score(X_test, y_test)\n<\/pre><\/div>\n\n\n
0.9230769230769231<\/p>\n\n\n
\nlogreg.predict(X_test)\n<\/pre><\/div>\n\n\n
array([0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0])<\/p>\n\n\n\n
\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u306e\u65b9\u304c\u826f\u3044\u30b9\u30b3\u30a2\u306b\u306a\u3063\u305f\u3002\u3057\u304b\u3057\u3001\u904e\u5b66\u7fd2\u3092\u8d77\u3053\u3057\u3066\u3044\u308b\u53ef\u80fd\u6027\u3082\u3042\u308b\u3002<\/p>\n\n\n\n
\u307e\u3068\u3081<\/h2>\n\n\n\n
 Random Forest \u304c\u4e00\u756a\u826f\u3044\u7d50\u679c\u306b\u306a\u308b\u3068\u4e88\u60f3\u3057\u3066\u3044\u305f\u304c\u3001\u9055\u3063\u305f\u3002
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u5927\u4f53\u554f\u984c\u306a\u3055\u305d\u3046\u306a\u306e\u3067\u3001\u3053\u306e\u554f\u984c\u3092\u89e3\u304f\u306b\u306f\u3069\u306e\u5206\u985e\u5668\u304c\u9069\u3057\u3066\u3044\u308b\u306e\u304b\u3001\u307e\u305f\u6700\u9069\u306a\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u3092\u63a2\u3059\u4f5c\u696d\u306f\u6b21\u56de\u884c\u3044\u305f\u3044\u3068\u601d\u3046\u3002<\/p>\n\n\n\n
\u8907\u6570\u306e\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u6bd4\u8f03\u3059\u308b<\/a>\u3078\u7d9a\u304f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
Recommendation System\u3092\u8003\u3048\u308b\u306e\u7d9a\u304d\u3002 \u66f4\u65b0\u5c65\u6b74\uff1a2021\/03\/19 \u30c7\u30fc\u30bf\u51e6\u7406\u306b\u8aa4\u308a\u304c\u3042\u3063\u305f\u305f\u3081\u8a02\u6b63 \u521d\u3081\u306b \u5c0f\u8aac\u3092\u30e6\u30fc\u30b6\u30fc\u306b\u63a8\u85a6\u3059\u308b\u304b\u3057\u306a\u3044\u304b\u3067\u5206\u3051\u305f\u3044\u306e\u3067\u3001\u3053\u308c\u306f\u5206\u985e\u554f\u984c\u3068\u306a\u308b\u3002\u5206\u985e\u554f\u984c\u306b\u3064\u3044 … “Recommendation System\u3092\u8003\u3048\u308b \u305d\u306e\uff12” \u306e<\/span>\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[41],"tags":[46,51],"class_list":["post-608","post","type-post","status-publish","format-standard","hentry","category-python","tag-46","tag-51"],"yoast_head":"\nRecommendation System\u3092\u8003\u3048\u308b \u305d\u306e\uff12 - 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