{"id":479671,"date":"2023-08-09T10:43:16","date_gmt":"2023-08-09T10:43:16","guid":{"rendered":""},"modified":"2023-09-05T11:19:19","modified_gmt":"2023-09-05T11:19:19","slug":"wide-and-deep-learning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/kr\/wiki\/wide-and-deep-learning\/","title":{"rendered":"\uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd"},"content":{"rendered":"<p>\uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd\uc740 \uad11\ubc94\uc704\ud55c \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc5d0\uc11c \ud6a8\uc728\uc801\uc73c\ub85c \ud559\uc2b5\ud558\uace0 \uc77c\ubc18\ud654\ud558\ub3c4\ub85d \uc124\uacc4\ub41c \uba38\uc2e0 \ub7ec\ub2dd \ubaa8\ub378 \ud074\ub798\uc2a4\uc785\ub2c8\ub2e4. \uc774 \uc811\uadfc \ubc29\uc2dd\uc740 \uc120\ud615 \ubaa8\ub378\uacfc \ub525 \ub7ec\ub2dd\uc744 \uacb0\ud569\ud558\uc5ec \uc554\uae30\uc640 \uc77c\ubc18\ud654\uac00 \ubaa8\ub450 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/p>\n<h2>\uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd\uc758 \uc720\ub798\uc640 \ucd5c\ucd08\uc758 \uc5b8\uae09\uc758 \uc5ed\uc0ac<\/h2>\n<p>\uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd(Wide and Deep Learning)\uc774\ub77c\ub294 \uac1c\ub150\uc740 2016\ub144 Google \uc5f0\uad6c\uc9c4\uc5d0 \uc758\ud574 \ucc98\uc74c \uc18c\uac1c\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \uc544\uc774\ub514\uc5b4\ub294 \ud559\uc2b5\uc758 \ub450 \uac00\uc9c0 \uc8fc\uc694 \uce21\uba74\uc778 \uc554\uae30\uc640 \uc77c\ubc18\ud654 \uc0ac\uc774\uc758 \uaca9\ucc28\ub97c \ud574\uc18c\ud558\ub294 \uac83\uc774\uc5c8\uc2b5\ub2c8\ub2e4. \uc5f0\uad6c\uc6d0\ub4e4\uc740 \uc120\ud615 \ubaa8\ub378(\uc640\uc774\ub4dc)\uacfc \uc2ec\uce35 \uc2e0\uacbd\ub9dd(\uae4a\uc740)\uc758 \uc870\ud569\uc744 \ud65c\uc6a9\ud558\uc5ec \ud559\uc2b5 \ud504\ub85c\uc138\uc2a4\ub97c \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \uac83\uc744 \ubaa9\ud45c\ub85c \ud588\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ud2b9\ud788 \uc0ac\uc6a9\uc790 \uc120\ud638\ub3c4\ub97c \uae30\uc5b5\ud558\uba74\uc11c \uc0c8\ub85c\uc6b4 \ucf58\ud150\uce20\ub97c \ucd94\ucc9c\ud558\ub824\ub294 YouTube\uc640 \uac19\uc740 \ucd94\ucc9c \uc2dc\uc2a4\ud15c\uc5d0 \uc801\uc6a9\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>\uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd\uc5d0 \ub300\ud55c \uc790\uc138\ud55c \uc815\ubcf4: \uc8fc\uc81c \ud655\uc7a5<\/h2>\n<p>\uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd\uc5d0\ub294 \ub370\uc774\ud130 \ud328\ud134\uc758 \uc77c\ubc18\ud654\ub97c \uac00\ub2a5\ud558\uac8c \ud558\ub294 \ub525 \ub7ec\ub2dd \ubaa8\ub378\uacfc \ud568\uaed8 \ub370\uc774\ud130\ub97c \uae30\uc5b5\ud560 \uc218 \uc788\ub294 \uc640\uc774\ub4dc \uc120\ud615 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/p>\n<h3>\uad6c\uc131\uc694\uc18c<\/h3>\n<ul>\n<li><strong>\uc640\uc774\ub4dc \ucef4\ud3ec\ub10c\ud2b8<\/strong>: \ud2b9\uc815 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8, \uc0c1\uad00 \uad00\uacc4 \ubc0f \ud2b9\uc9d5\uc744 \uae30\uc5b5\ud558\ub294 \ub370 \uc911\uc810\uc744 \ub461\ub2c8\ub2e4.<\/li>\n<li><strong>\uae4a\uc740 \uad6c\uc131 \uc694\uc18c<\/strong>: \ub370\uc774\ud130\uc758 \ub192\uc740 \uc218\uc900\uc758 \ucd94\uc0c1\ud654\ub97c \uc77c\ubc18\ud654\ud558\uace0 \ud559\uc2b5\ud558\ub294 \uc791\uc5c5\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h3>\uc751\uc6a9<\/h3>\n<ul>\n<li><strong>\ucd94\ucc9c \uc2dc\uc2a4\ud15c<\/strong>: \ub9de\ucda4\ud615 \ucd94\ucc9c\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uac80\uc0c9 \uc21c\uc704<\/strong>: \uc0ac\uc6a9\uc790 \ud328\ud134\uc744 \uc774\ud574\ud558\uc5ec \uac80\uc0c9 \uacb0\uacfc\ub97c \ud5a5\uc0c1\uc2dc\ud0b5\ub2c8\ub2e4.<\/li>\n<li><strong>\uc608\uce21 \ubd84\uc11d<\/strong>: \ubcf5\uc7a1\ud55c \uc608\uce21 \uc791\uc5c5\uc5d0 \uc640\uc774\ub4dc \uc564 \ub525 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>\uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd\uc758 \ub0b4\ubd80 \uad6c\uc870: \uc791\ub3d9 \ubc29\uc2dd<\/h2>\n<p>\uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd \ubaa8\ub378\uc758 \uc544\ud0a4\ud14d\ucc98\ub294 \ub450 \uac00\uc9c0 \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4.<\/p>\n<ol>\n<li><strong>\uc640\uc774\ub4dc \ucef4\ud3ec\ub10c\ud2b8<\/strong>: \uc785\ub825 \ud2b9\uc131\uc744 \ucd9c\ub825\uc5d0 \uc9c1\uc811 \uc5f0\uacb0\ud558\ub294 \uc120\ud615 \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc774 \ubd80\ubd84\uc5d0\uc11c\ub294 \ud76c\ubc15\ud558\uace0 \uc6d0\uc2dc \uc785\ub825 \uae30\ub2a5\uc744 \ub2e4\ub8e8\uba70 \ud2b9\uc815 \ud328\ud134\uc744 \ucea1\ucc98\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uae4a\uc740 \uad6c\uc131 \uc694\uc18c<\/strong>: \uc5ec\ub7ec \uac1c\uc758 \ud788\ub4e0 \ub808\uc774\uc5b4\ub85c \uad6c\uc131\ub41c \uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc785\ub2c8\ub2e4. \uc774 \ubd80\ubd84\uc740 \ucd94\uc0c1\uc801\uc778 \ud328\ud134\uc744 \uc774\ud574\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4.<\/li>\n<\/ol>\n<p>\uc774\ub7ec\ud55c \uad6c\uc131 \uc694\uc18c\ub294 \ud568\uaed8 \uc554\uae30\uc640 \uc77c\ubc18\ud654\uc758 \uade0\ud615\uc744 \ub9de\ucd94\ub294 \uacb0\ud569\ub41c \uc608\uce21\uc744 \ud615\uc131\ud569\ub2c8\ub2e4.<\/p>\n<h2>\uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd\uc758 \uc8fc\uc694 \ud2b9\uc9d5 \ubd84\uc11d<\/h2>\n<ul>\n<li><strong>\uc720\uc5f0\uc131<\/strong>: \ub2e4\uc591\ud55c \ud559\uc2b5 \uacfc\uc81c\uc5d0 \uc801\ud569\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ud655\uc7a5\uc131<\/strong>: \ud06c\uace0 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130\uc138\ud2b8\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \ucc98\ub9ac\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uade0\ud615 \uc7a1\ud78c \ud559\uc2b5<\/strong>: \uc554\uae30\uc640 \uc77c\ubc18\ud654\uc758 \uc7a5\uc810\uc744 \uacb0\ud569\ud55c \uc81c\ud488\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\ud5a5\uc0c1\ub41c \uc608\uce21<\/strong>: \ub3c5\ub9bd\ud615 \ubaa8\ub378\uc5d0 \ube44\ud574 \ub6f0\uc5b4\ub09c \uc608\uce21 \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>\uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd\uc758 \uc720\ud615<\/h2>\n<p>\uc640\uc774\ub4dc \ubc0f \ub525 \ub7ec\ub2dd \ubaa8\ub378\uc5d0\ub294 \ub2e4\uc591\ud55c \ubcc0\ud615\uacfc \uad6c\ud604\uc774 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uba87 \uac00\uc9c0 \uc77c\ubc18\uc801\uc778 \uc720\ud615\uc744 \uc694\uc57d\ud55c \ud45c\uc785\ub2c8\ub2e4.<\/p>\n<table>\n<thead>\n<tr>\n<th>\uc720\ud615<\/th>\n<th>\uc640\uc774\ub4dc \ucef4\ud3ec\ub10c\ud2b8<\/th>\n<th>\uae4a\uc740 \uad6c\uc131 \uc694\uc18c<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\ud45c\uc900 \ubaa8\ub378<\/td>\n<td>\uc120\ud615 \ubaa8\ub378<\/td>\n<td>\uc2ec\uce35 \uc2e0\uacbd\ub9dd<\/td>\n<\/tr>\n<tr>\n<td>\ud558\uc774\ube0c\ub9ac\ub4dc \ubaa8\ub378<\/td>\n<td>\ub9de\ucda4\ud615 \uc120\ud615 \ubaa8\ub378<\/td>\n<td>\ucee8\ubcfc\ub8e8\uc154\ub110 \uc2e0\uacbd\ub9dd<\/td>\n<\/tr>\n<tr>\n<td>\ub3c4\uba54\uc778\ubcc4 \ubaa8\ub378<\/td>\n<td>\uc0b0\uc5c5\ubcc4 \ub85c\uc9c1<\/td>\n<td>\uc21c\ud658 \uc2e0\uacbd\ub9dd<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd \ud65c\uc6a9 \ubc29\ubc95, \ubb38\uc81c \ubc0f \uc194\ub8e8\uc158<\/h2>\n<h3>\uc6a9\ubc95<\/h3>\n<ul>\n<li><strong>\ube44\uc988\ub2c8\uc2a4 \ubd84\uc11d<\/strong>: \uace0\uac1d \ud589\ub3d9\uc744 \uc608\uce21\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ubcf4\uac74 \uc758\ub8cc<\/strong>: \ub9de\ucda4\ud615 \uce58\ub8cc \uacc4\ud68d\uc744 \uc218\ub9bd\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc804\uc790\uc0c1\uac70\ub798<\/strong>: \uc0c1\ud488 \ucd94\ucc9c \uac15\ud654.<\/li>\n<\/ul>\n<h3>\ubb38\uc81c \ubc0f \ud574\uacb0 \ubc29\ubc95<\/h3>\n<ul>\n<li><strong>\uacfc\uc801\ud569<\/strong>: \uc801\uc808\ud55c \uc815\uaddc\ud654\ub97c \ud1b5\ud574 \ud574\uacb0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\ubcf5\uc7a1\uc131<\/strong>: \ubaa8\ub378 \uc544\ud0a4\ud14d\ucc98\uc758 \ub2e8\uc21c\ud654 \ubc0f \ucd5c\uc801\ud654\uac00 \ub3c4\uc6c0\uc774 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>\uc8fc\uc694 \ud2b9\uc9d5 \ubc0f \uae30\ud0c0 \uc720\uc0ac \uc6a9\uc5b4\uc640\uc758 \ube44\uad50<\/h2>\n<ul>\n<li><strong>\ub525\ub7ec\ub2dd\uacfc \ube44\uad50<\/strong>: \uc554\uae30\uc5d0 \ub354 \uc911\uc810\uc744 \ub450\uc5b4 \ud2b9\uc815 \ud328\ud134\uacfc \ucd94\uc0c1 \ud328\ud134 \uac04\uc758 \uade0\ud615\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc120\ud615 \ubaa8\ub378\uacfc \ube44\uad50<\/strong>: \ud328\ud134\uc744 \uc77c\ubc18\ud654\ud558\ub294 \ub525\ub7ec\ub2dd\uc758 \ud798\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>\uc640\uc774\ub4dc\uc564\ub525\ub7ec\ub2dd\uacfc \uad00\ub828\ub41c \ubbf8\ub798\uc758 \uad00\uc810\uacfc \uae30\uc220<\/h2>\n<p>\uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd\uc758 \ubbf8\ub798\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uc9c0\uc18d\uc801\uc778 \uc5f0\uad6c\ub97c \ud1b5\ud574 \uc720\ub9dd\ud574 \ubcf4\uc785\ub2c8\ub2e4.<\/p>\n<ul>\n<li><strong>AutoML<\/strong>: Wide, Deep \ubaa8\ub378\uc758 \uc124\uacc4\ub97c \uc790\ub3d9\ud654\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc804\uc774 \ud559\uc2b5<\/strong>: \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \ub2e4\uc591\ud55c \ub3c4\uba54\uc778\uc5d0 \uc801\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc5e3\uc9c0 \ucef4\ud4e8\ud305<\/strong>: \uc2e4\uc2dc\uac04 \ubd84\uc11d\uc744 \uc704\ud574 \ub370\uc774\ud130 \uc18c\uc2a4\uc5d0 \uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd\uc744 \ub354 \uac00\uae4c\uc774 \uac00\uc838\uc635\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>\ud504\ub85d\uc2dc \uc11c\ubc84\ub97c \uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd\uc5d0 \uc0ac\uc6a9\ud558\uac70\ub098 \uc5f0\uacb0\ud558\ub294 \ubc29\ubc95<\/h2>\n<p>OneProxy\uc640 \uac19\uc740 \ud504\ub85d\uc2dc \uc11c\ubc84\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ubc29\uc2dd\uc73c\ub85c \uad11\ubc94\uc704\ud558\uace0 \uc2ec\uce35\uc801\uc778 \ud559\uc2b5\uc5d0 \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<ul>\n<li><strong>\ub370\uc774\ud130 \uc218\uc9d1<\/strong>: \ub300\uaddc\ubaa8 \ub370\uc774\ud130\ub97c \uc81c\ud55c \uc5c6\uc774 \uc218\uc9d1\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uac1c\uc778\uc815\ubcf4 \ubcf4\ud638<\/strong>: \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\ub294 \ub3d9\uc548 \uc775\uba85\uc131\uc744 \ubcf4\uc7a5\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ub85c\ub4dc \ubc38\ub7f0\uc2f1<\/strong>: \ubd84\uc0b0 \ud6c8\ub828 \uc911 \ub178\ub4dc \uac04 \ub370\uc774\ud130 \uc804\uc1a1\uc744 \ud6a8\uc728\uc801\uc73c\ub85c \uad00\ub9ac\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>\uad00\ub828\ub41c \ub9c1\ud06c\ub4e4<\/h2>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1606.07792\" target=\"_new\" rel=\"noopener nofollow\">\uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd\uc5d0 \uad00\ud55c Google \uc5f0\uad6c \ub17c\ubb38<\/a><\/li>\n<li><a href=\"https:\/\/www.tensorflow.org\/tutorials\/wide_and_deep\" target=\"_new\" rel=\"noopener nofollow\">TensorFlow \uad6c\ud604 \uac00\uc774\ub4dc<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/kr\/\" target=\"_new\" rel=\"noopener\">OneProxy \uc6f9\uc0ac\uc774\ud2b8<\/a> \uae30\uacc4 \ud559\uc2b5\uc758 \ud504\ub85d\uc2dc \uc11c\ubc84 \ud65c\uc6a9\uc5d0 \ub300\ud55c \uc790\uc138\ud55c \ub0b4\uc6a9\uc744 \uc54c\uc544\ubcf4\uc138\uc694.<\/li>\n<\/ul>\n<p>\uc120\ud615 \ubaa8\ub378\uacfc \uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc758 \uc7a5\uc810\uc744 \uacb0\ud569\ud55c \uc640\uc774\ub4dc \uc564 \ub525 \ub7ec\ub2dd\uc740 \ub2e4\uc591\ud55c \uae30\uacc4 \ud559\uc2b5 \ubb38\uc81c\uc5d0 \ub300\ud55c \uc720\uc5f0\ud558\uace0 \uac15\ub825\ud55c \uc811\uadfc \ubc29\uc2dd\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \ud504\ub85d\uc2dc \uc11c\ubc84\uc640 \uac19\uc740 \uae30\uc220\uacfc\uc758 \ud1b5\ud569\uc740 \ube60\ub974\uac8c \ubc1c\uc804\ud558\ub294 \uc778\uacf5 \uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \uc801\uc6a9 \uac00\ub2a5\uc131\uacfc \ud6a8\uc728\uc131\uc744 \ub354\uc6b1 \ud655\ub300\ud569\ub2c8\ub2e4.<\/p>","protected":false},"featured_media":470940,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479671","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Wide and Deep Learning<\/mark>","faq_items":[{"question":"What is Wide and Deep Learning?","answer":"<p>Wide and Deep Learning is a machine learning model that combines linear models with deep learning. This combination allows the model to memorize specific data patterns while also generalizing across data, making it effective for various applications like recommendation systems, search ranking, and predictive analytics.<\/p>"},{"question":"When was Wide and Deep Learning first introduced?","answer":"<p>Wide and Deep Learning was first introduced by Google researchers in 2016. The concept was developed to bridge the gap between memorization and generalization in machine learning, and it was initially applied in recommendation systems like YouTube.<\/p>"},{"question":"What are the key components of Wide and Deep Learning?","answer":"<p>The key components of Wide and Deep Learning include the Wide Component, a linear model focusing on memorizing specific data points, and the Deep Component, a deep neural network working on generalizing and learning high-level abstractions in the data.<\/p>"},{"question":"How is Wide and Deep Learning used in recommendation systems?","answer":"<p>In recommendation systems, Wide and Deep Learning helps to recommend new content while remembering user preferences. The wide part memorizes user behavior and specific correlations, while the deep part generalizes this data to recommend content that might align with user interests.<\/p>"},{"question":"What types of Wide and Deep Learning models exist?","answer":"<p>There are different variations of wide and deep learning models, including Standard Models with general linear and deep neural networks, Hybrid Models that can be customized, and Domain-specific Models with industry-specific logic and networks.<\/p>"},{"question":"What are some problems and solutions related to Wide and Deep Learning?","answer":"<p>Some problems include overfitting, which can be addressed by proper regularization, and complexity, which can be alleviated by simplifying and optimizing the model architecture.<\/p>"},{"question":"How are proxy servers like OneProxy associated with Wide and Deep Learning?","answer":"<p>Proxy servers like OneProxy can be utilized in wide and deep learning for purposes such as data collection, privacy preservation, and load balancing. They enable the gathering of large-scale data without restrictions and ensure anonymity while training models.<\/p>"},{"question":"What are the future perspectives related to Wide and Deep Learning?","answer":"<p>The future of wide and deep learning includes ongoing research in areas like AutoML, transfer learning, and edge computing. The integration of these technologies could lead to automating the design of models, applying pre-trained models to various domains, and bringing learning closer to data sources for real-time analytics.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki\/479671","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki\/479671\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/media\/470940"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/media?parent=479671"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}