Web Scale Competitor Discovery Using Mutual Information
The web with its rapid expansion has become an excellent resource for gathering information and people’s opinion. A company owner wants to know who is the competitor, and a customer also wants to know which company provides similar product or service to what he/she is in want of. This paper proposes...
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creator | Li, Rui Bao, Shenghua Wang, Jin Liu, Yuanjie Yu, Yong |
description | The web with its rapid expansion has become an excellent resource for gathering information and people’s opinion. A company owner wants to know who is the competitor, and a customer also wants to know which company provides similar product or service to what he/she is in want of. This paper proposes an approach based on mutual information, which focuses on mining competitors of the entity(such as company, product, person ) from the web. The proposed techniques first extract a set of candidates of the input entity, and then rank them according to the comparability, and finally find and organize the reviews related to both original entity and its competitors. A novel system called ”CoDis” based upon these techniques is implemented, which is able to automate the tedious process in a domain-independent and web-scale dynamical manner. In the experiment we use 32 different entities distributed in varied domains as inputs and the CoDis discovers 143 competitors. The experimental results show that the proposed techniques are highly effective. |
doi_str_mv | 10.1007/11811305_87 |
format | Book Chapter |
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A company owner wants to know who is the competitor, and a customer also wants to know which company provides similar product or service to what he/she is in want of. This paper proposes an approach based on mutual information, which focuses on mining competitors of the entity(such as company, product, person ) from the web. The proposed techniques first extract a set of candidates of the input entity, and then rank them according to the comparability, and finally find and organize the reviews related to both original entity and its competitors. A novel system called ”CoDis” based upon these techniques is implemented, which is able to automate the tedious process in a domain-independent and web-scale dynamical manner. In the experiment we use 32 different entities distributed in varied domains as inputs and the CoDis discovers 143 competitors. The experimental results show that the proposed techniques are highly effective.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540370250</identifier><identifier>ISBN: 9783540370253</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540370269</identifier><identifier>EISBN: 9783540370260</identifier><identifier>DOI: 10.1007/11811305_87</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Brand Product ; Domain Information ; Football Club ; Mutual Information ; Search Engine</subject><ispartof>Advanced Data Mining and Applications, 2006, p.798-808</ispartof><rights>Springer-Verlag Berlin Heidelberg 2006</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><relation>Lecture Notes in Computer Science</relation></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11811305_87$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11811305_87$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>775,776,780,789,27902,38232,41418,42487</link.rule.ids></links><search><contributor>Zaïane, Osmar R.</contributor><contributor>Li, Xue</contributor><contributor>Li, Zhanhuai</contributor><creatorcontrib>Li, Rui</creatorcontrib><creatorcontrib>Bao, Shenghua</creatorcontrib><creatorcontrib>Wang, Jin</creatorcontrib><creatorcontrib>Liu, Yuanjie</creatorcontrib><creatorcontrib>Yu, Yong</creatorcontrib><title>Web Scale Competitor Discovery Using Mutual Information</title><title>Advanced Data Mining and Applications</title><description>The web with its rapid expansion has become an excellent resource for gathering information and people’s opinion. A company owner wants to know who is the competitor, and a customer also wants to know which company provides similar product or service to what he/she is in want of. This paper proposes an approach based on mutual information, which focuses on mining competitors of the entity(such as company, product, person ) from the web. The proposed techniques first extract a set of candidates of the input entity, and then rank them according to the comparability, and finally find and organize the reviews related to both original entity and its competitors. A novel system called ”CoDis” based upon these techniques is implemented, which is able to automate the tedious process in a domain-independent and web-scale dynamical manner. In the experiment we use 32 different entities distributed in varied domains as inputs and the CoDis discovers 143 competitors. The experimental results show that the proposed techniques are highly effective.</description><subject>Brand Product</subject><subject>Domain Information</subject><subject>Football Club</subject><subject>Mutual Information</subject><subject>Search Engine</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540370250</isbn><isbn>9783540370253</isbn><isbn>3540370269</isbn><isbn>9783540370260</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2006</creationdate><recordtype>book_chapter</recordtype><sourceid/><recordid>eNqVjrsKwkAQRccXGB-VP7CtRXQmk7im9oEWViqWSyKrRGNWdqPg36sgaOttbnHPhQPQIxwQohwSjYkYIzWWFWhxFCJLDEZxFTwaEfnMYVz7DhHWwUPGwI9lyE3oOnfCV5giGQQeyJ1OxXqf5FpMzOWqy6w0Vkwztzd3bR9i67LiKFa38pbkYlkcjL0kZWaKDjQOSe5099Nt6M9nm8nCd1f7emirUmPOThGqt7b60eZ_2CfXsUFW</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Li, Rui</creator><creator>Bao, Shenghua</creator><creator>Wang, Jin</creator><creator>Liu, Yuanjie</creator><creator>Yu, Yong</creator><general>Springer Berlin Heidelberg</general><scope/></search><sort><creationdate>2006</creationdate><title>Web Scale Competitor Discovery Using Mutual Information</title><author>Li, Rui ; Bao, Shenghua ; Wang, Jin ; Liu, Yuanjie ; Yu, Yong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-springer_books_10_1007_11811305_873</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Brand Product</topic><topic>Domain Information</topic><topic>Football Club</topic><topic>Mutual Information</topic><topic>Search Engine</topic><toplevel>online_resources</toplevel><creatorcontrib>Li, Rui</creatorcontrib><creatorcontrib>Bao, Shenghua</creatorcontrib><creatorcontrib>Wang, Jin</creatorcontrib><creatorcontrib>Liu, Yuanjie</creatorcontrib><creatorcontrib>Yu, Yong</creatorcontrib></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Rui</au><au>Bao, Shenghua</au><au>Wang, Jin</au><au>Liu, Yuanjie</au><au>Yu, Yong</au><au>Zaïane, Osmar R.</au><au>Li, Xue</au><au>Li, Zhanhuai</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Web Scale Competitor Discovery Using Mutual Information</atitle><btitle>Advanced Data Mining and Applications</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2006</date><risdate>2006</risdate><spage>798</spage><epage>808</epage><pages>798-808</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540370250</isbn><isbn>9783540370253</isbn><eisbn>3540370269</eisbn><eisbn>9783540370260</eisbn><abstract>The web with its rapid expansion has become an excellent resource for gathering information and people’s opinion. A company owner wants to know who is the competitor, and a customer also wants to know which company provides similar product or service to what he/she is in want of. This paper proposes an approach based on mutual information, which focuses on mining competitors of the entity(such as company, product, person ) from the web. The proposed techniques first extract a set of candidates of the input entity, and then rank them according to the comparability, and finally find and organize the reviews related to both original entity and its competitors. A novel system called ”CoDis” based upon these techniques is implemented, which is able to automate the tedious process in a domain-independent and web-scale dynamical manner. In the experiment we use 32 different entities distributed in varied domains as inputs and the CoDis discovers 143 competitors. The experimental results show that the proposed techniques are highly effective.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11811305_87</doi></addata></record> |
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subjects | Brand Product Domain Information Football Club Mutual Information Search Engine |
title | Web Scale Competitor Discovery Using Mutual Information |
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