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HITS（Hyperlink-Induced Topic Search）是一種基于鏈接的搜索引擎排名算法，與其他排名算法如PageRank一樣，它的主要目的是將用戶(hù)查詢(xún)與集合的文檔匹配，使得最相關(guān)的文檔被檢索出來(lái)，排在前面。事實(shí)上，網(wǎng)頁(yè)根據(jù)網(wǎng)頁(yè)內(nèi)部和外部因素來(lái)排序。HITS算法包括兩個(gè)重要指標(biāo)：Authority和Hub，體現(xiàn)網(wǎng)頁(yè)的相關(guān)性，指導(dǎo)搜索引擎排序，即把相關(guān)性高的網(wǎng)頁(yè)排在前面。 HI

Public @ 2023-03-02 20:01:00

HITS算法

威海鏈接原理威海HITS算法
1390

HITS算法（Hypertext-Induced Topic Selection），也稱(chēng)為Hubs and Authorities algorithm，是一種用來(lái)計(jì)算圖中節(jié)點(diǎn)相對(duì)重要性的算法。它是Larry Page and Sergey Brin 在1998年首次提出，以提高他們搜索引擎Google的網(wǎng)頁(yè)檢索準(zhǔn)確性。HITS算法通過(guò)分析不同節(jié)點(diǎn)間的連接關(guān)系來(lái)鑒定節(jié)點(diǎn)的重要性分?jǐn)?shù)：一個(gè)節(jié)點(diǎn)

Public @ 2023-02-27 07:00:50

HITS算法

威海鏈接原理威海HITS算法
1211

HITS算法是一種采用迭代計(jì)算的網(wǎng)絡(luò)算法，用于計(jì)算網(wǎng)絡(luò)中節(jié)點(diǎn)的權(quán)重。該算法主要用于網(wǎng)絡(luò)信息檢索等計(jì)算技術(shù)。HITS算法與傳統(tǒng)的關(guān)鍵詞檢索不同，關(guān)鍵詞檢索是以詞語(yǔ)為基礎(chǔ)，以文檔為主要檢索對(duì)象，結(jié)果通過(guò)匹配檢索關(guān)鍵詞的文檔及其相關(guān)性程序來(lái)返回結(jié)果。而HITS算法則是以鏈接為基礎(chǔ)，以網(wǎng)頁(yè)為主要檢索對(duì)象，通過(guò)計(jì)算每個(gè)頁(yè)面的被鏈接的頻度和鏈接其它頁(yè)面的重要程度來(lái)決定查詢(xún)結(jié)果的排序。該算法基于“authr

Public @ 2023-02-25 06:01:28

HITS算法

威海鏈接原理威海HITS算法
1272

HITS 算法是由康奈爾大學(xué)( Cornell University ) 的Jon Kleinberg 博士于1997 年首先提出的,為IBM 公司阿爾馬登研究中心( IBM Almaden Research Center) 的名為“CLEVER”的研究項(xiàng)目中的一部分。英文全稱(chēng)Hyperlink-Induced Topic Search算法由來(lái)HITS 算法是由康奈爾大學(xué)( Cornell Uni

Public @ 2022-03-03 15:39:12

HITS鏈接分析算法

威海鏈接原理威海HITS算法
1268

HITS(Hyperlink - Induced Topic Search)鏈接分析算法誕生在1997年，該算法是由康奈爾大學(xué)中的一位博士提出，并且該算法沿用于全球多個(gè)搜索引擎當(dāng)中。當(dāng)然，不同的搜索引擎針對(duì)于該算法的側(cè)重點(diǎn)和內(nèi)部公式都有不一的算法結(jié)構(gòu)調(diào)整，并且HITS算法也是鏈接分析中最為重要的基礎(chǔ)算法之一。對(duì)于目前國(guó)內(nèi)的整體搜索引擎而言，百度作為國(guó)內(nèi)的搜索引擎領(lǐng)頭羊，我會(huì)帶著HITS的公開(kāi)文檔針

Public @ 2009-11-28 15:39:11

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[ DB ] CONNECT:[ UseTime:0.023743s ] mysql:dbname=briline.net;host=106.14.77.182;port=3306;charset=utf8
[ SQL ] SHOW COLUMNS FROM `ob_config` [ RunTime:0.016807s ]
[ SQL ] SELECT * FROM `ob_config` [ RunTime:0.015795s ]
[ 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:0.016817s ]
[ SQL ] SELECT `id`,`name`,`addon_list` FROM `ob_hook` [ RunTime:0.015696s ]
[ 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:0.016656s ]
[ SQL ] SELECT COUNT(*) AS tp_count FROM `ob_article` WHERE `category_id` = 12 AND `tags` = '威海HITS算法' AND `status` <> -1 LIMIT 1 [ RunTime:0.025031s ]
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[ SQL ] SELECT * FROM `ob_article` WHERE `category_id` = 12 AND `tags` = '威海HITS算法' AND `status` <> -1 ORDER BY create_time desc LIMIT 0,10 [ RunTime:0.030657s ]
[ EXPLAIN : array ( 'id' => 1, 'select_type' => 'SIMPLE', 'table' => 'ob_article', 'type' => 'ALL', 'possible_keys' => NULL, 'key' => NULL, 'key_len' => NULL, 'ref' => NULL, 'rows' => 8035, 'extra' => 'Using where; Using filesort', ) ]
[ SQL ] select * from `ob_article_tongji` where category_id=12 and mark_type='cate' order by times desc limit 15 [ RunTime:0.016155s ]
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[ SQL ] select * from `ob_article_tongji` where category_id=12 and mark_type='tags' order by times desc limit 100 [ RunTime:0.016241s ]
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[ SQL ] select * from `ob_article_tongji` where category_id=12 and mark_type='tags' order by rand() limit 30 [ RunTime:0.016398s ]
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