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TrustRank算法

TrustRank算法是一個(gè)用于檢測(cè)Web圖譜（Web graphs）的可靠性和安全性的算法。它是由斯坦福大學(xué)的一組研究人員開發(fā)和發(fā)布的。TrustRank的思想是基于之前的 PageRank算法，但是與PageRank不同的是，TrustRank使用了人工干預(yù)的方式，將一些網(wǎng)站標(biāo)記為可信、不可信或者中等可信度。然后，利用這些標(biāo)記，計(jì)算每個(gè)網(wǎng)站的信任值，表示對(duì)這個(gè)網(wǎng)站的訪問與使用是否安全和可靠。這個(gè)算法的核心思想是，信任的傳遞。也就是說，如果一個(gè)網(wǎng)站被標(biāo)記為可信，則從這個(gè)網(wǎng)站到其鏈接的網(wǎng)站的信任值也會(huì)增加。反之亦然，如果一個(gè)網(wǎng)站被標(biāo)記為不可信，則到其鏈接的其他網(wǎng)站的信任值也會(huì)受到影響。TrustRank算法可以通過對(duì)Web圖譜進(jìn)行分析和可視化，幫助用戶識(shí)別可信度高的網(wǎng)站和不受信任的網(wǎng)站，以保護(hù)用戶的信息安全和網(wǎng)絡(luò)安全。

Public @ 2023-04-05 05:50:11

Matt Cutts談PR和PR更新

威海鏈接原理威海PR
2356

大家都注意到前兩天Toolbar(工具條)PR已經(jīng)更新了。Matt Cutts發(fā)了一個(gè)帖子，回答一些關(guān)于PR和PR更新的問題。問：我很好奇PR值在內(nèi)部是怎樣存儲(chǔ)的？是小數(shù)，就像人們猜測(cè)的，還是整數(shù)？答：把它想為小數(shù)更準(zhǔn)確。當(dāng)然目前我們內(nèi)部的PR計(jì)算比toolbar顯示的0－10要長的多。問：我需要知道這個(gè)嗎？我知道PR能告訴我什么，我為什么要在乎？也就是說對(duì)普通網(wǎng)站來說，PR有什么目的？答：我想這

Public @ 2012-12-27 15:39:04

TrustRank算法

TrustRank是一種基于Web搜索的信息檢索技術(shù)，用于挖掘有質(zhì)量的網(wǎng)頁，并將其置于搜索結(jié)果的頂部。它可以準(zhǔn)確地將有質(zhì)量的網(wǎng)頁與搜索詞相關(guān)聯(lián)，從而過濾掉某些無關(guān)的鏈接，讓有質(zhì)量的網(wǎng)頁出現(xiàn)在搜索結(jié)果中。 TrustRank算法通過跟蹤搜索引擎向用戶展示的鏈接的信譽(yù)來確定搜索結(jié)果的順序。同時(shí)它會(huì)檢測(cè)用戶的反饋，比如點(diǎn)擊量，瀏覽時(shí)間，以及用戶評(píng)價(jià)，用于調(diào)整頁面排列順序。這樣的話，用戶就可以更加準(zhǔn)

Public @ 2023-02-27 09:48:21

TrustRank算法

TrustRank算法是早年基于鏈接關(guān)系的排名算法。TrustRank可以翻譯為“信任指數(shù)”。算法簡(jiǎn)介TrustRank算法TrustRank算法最初來自于2004年斯坦福大學(xué)和雅虎的一項(xiàng)聯(lián)合研究，用來檢測(cè)垃圾網(wǎng)站，并且于2006年申請(qǐng)專利。TrustRank算法發(fā)明人還發(fā)表了一份專門的PDF文件，說明TrustRank算法的應(yīng)用。感興趣的讀者可以在下面這個(gè)網(wǎng)址下載PDF文件：TrustRank算

Public @ 2015-10-12 15:39:10

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基本文件流程錯(cuò)誤 SQL 調(diào)試

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[ DB ] CONNECT:[ UseTime:0.021751s ] mysql:dbname=briline.net;host=106.14.77.182;port=3306;charset=utf8
[ SQL ] SHOW COLUMNS FROM `ob_config` [ RunTime:0.016458s ]
[ SQL ] SELECT * FROM `ob_config` [ RunTime:0.014686s ]
[ EXPLAIN : array ( 'id' => 1, 'select_type' => 'SIMPLE', 'table' => 'ob_config', 'type' => 'ALL', 'possible_keys' => NULL, 'key' => NULL, 'key_len' => NULL, 'ref' => NULL, 'rows' => 25, 'extra' => NULL, ) ]
[ SQL ] SHOW COLUMNS FROM `ob_hook` [ RunTime:0.015542s ]
[ SQL ] SELECT `id`,`name`,`addon_list` FROM `ob_hook` [ RunTime:0.014597s ]
[ EXPLAIN : array ( 'id' => 1, 'select_type' => 'SIMPLE', 'table' => 'ob_hook', 'type' => 'ALL', 'possible_keys' => NULL, 'key' => NULL, 'key_len' => NULL, 'ref' => NULL, 'rows' => 3, 'extra' => NULL, ) ]
[ SQL ] SHOW COLUMNS FROM `ob_article` [ RunTime:0.015657s ]
[ SQL ] SELECT * FROM `ob_article` WHERE `id` = 6807 LIMIT 1 [ RunTime:0.014745s ]
[ EXPLAIN : array ( 'id' => 1, 'select_type' => 'SIMPLE', 'table' => 'ob_article', 'type' => 'const', 'possible_keys' => 'PRIMARY', 'key' => 'PRIMARY', 'key_len' => '4', 'ref' => 'const', 'rows' => 1, 'extra' => NULL, ) ]
[ SQL ] select * from `ob_article_tongji` where category_id=12 and mark_type='cate' order by times desc limit 15 [ RunTime:0.015016s ]
[ EXPLAIN : array ( 'id' => 1, 'select_type' => 'SIMPLE', 'table' => 'ob_article_tongji', 'type' => 'ALL', 'possible_keys' => NULL, 'key' => NULL, 'key_len' => NULL, 'ref' => NULL, 'rows' => 608, 'extra' => 'Using where; Using filesort', ) ]
[ SQL ] select * from `ob_article_tongji` where category_id=12 and mark_type='tags' order by times desc limit 100 [ RunTime:0.015175s ]
[ EXPLAIN : array ( 'id' => 1, 'select_type' => 'SIMPLE', 'table' => 'ob_article_tongji', 'type' => 'ALL', 'possible_keys' => NULL, 'key' => NULL, 'key_len' => NULL, 'ref' => NULL, 'rows' => 608, 'extra' => 'Using where; Using filesort', ) ]
[ SQL ] select * from `ob_article_tongji` where category_id=12 and mark_type='tags' order by rand() limit 30 [ RunTime:0.015728s ]
[ EXPLAIN : array ( 'id' => 1, 'select_type' => 'SIMPLE', 'table' => 'ob_article_tongji', 'type' => 'ALL', 'possible_keys' => NULL, 'key' => NULL, 'key_len' => NULL, 'ref' => NULL, 'rows' => 608, 'extra' => 'Using where; Using temporary; Using filesort', ) ]
[ SQL ] SELECT * FROM `ob_article` WHERE `id` = 6807 LIMIT 1 [ RunTime:0.014831s ]
[ EXPLAIN : array ( 'id' => 1, 'select_type' => 'SIMPLE', 'table' => 'ob_article', 'type' => 'const', 'possible_keys' => 'PRIMARY', 'key' => 'PRIMARY', 'key_len' => '4', 'ref' => 'const', 'rows' => 1, 'extra' => NULL, ) ]
[ SQL ] update `ob_article` set views=views+1 where id=6807 [ RunTime:0.018382s ]
[ SQL ] SELECT COUNT(*) AS tp_count FROM `ob_article` WHERE `category_id` = 12 AND `cate` = '威海鏈接原理' AND `status` <> -1 LIMIT 1 [ RunTime:0.024984s ]
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[ SQL ] SELECT * FROM `ob_article` WHERE `category_id` = 12 AND `cate` = '威海鏈接原理' AND `status` <> -1 ORDER BY rand() LIMIT 0,2 [ RunTime:0.031661s ]
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[ SQL ] SELECT COUNT(*) AS tp_count FROM `ob_article` WHERE `category_id` = 12 AND `tags` = '威海TrustRank算法' AND `status` <> -1 LIMIT 1 [ RunTime:0.021339s ]
[ EXPLAIN : array ( 'id' => 1, 'select_type' => 'SIMPLE', 'table' => 'ob_article', 'type' => 'ALL', 'possible_keys' => NULL, 'key' => NULL, 'key_len' => NULL, 'ref' => NULL, 'rows' => 9562, 'extra' => 'Using where', ) ]
[ SQL ] SELECT * FROM `ob_article` WHERE `category_id` = 12 AND `tags` = '威海TrustRank算法' AND `status` <> -1 ORDER BY rand() LIMIT 0,2 [ RunTime:0.030791s ]
[ EXPLAIN : array ( 'id' => 1, 'select_type' => 'SIMPLE', 'table' => 'ob_article', 'type' => 'ALL', 'possible_keys' => NULL, 'key' => NULL, 'key_len' => NULL, 'ref' => NULL, 'rows' => 9562, 'extra' => 'Using where; Using temporary; Using filesort', ) ]

0.496055s

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TrustRank算法

Matt Cutts談PR和PR更新

最新Google排名算法專利

TrustRank算法

TrustRank算法

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