UTMCrawler : crawling the E-business social network using genetic algorithm for relevant document searching

• Main Authors: Ibrahim, Siti Nurkhadijah Aishah, Selamat, Ali, Selamat, Mohd. Hafiz
• Format: Conference or Workshop Item
• Language: English
• Published: 2009
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/12466/1/SitiNurkhadijahAishah2008_CrawlingtheEBusinessSocialNetwork.pdf; http://eprints.utm.my/id/eprint/12466/

The increasing of online social network in the Internet has caused the explosion of search results from the search engines. According to the Google search engine statistics, until 2008 almost 1 trillion web pages have been indexing including the online social network website. Thus, how can we retrieve the massive online social network information with the exploded information accessible in the web? In this paper, we have designed the internet agent! crawler based genetic algorithm to retrieve the e-business web pages from the lelong.com.my, the Malaysia online auction website. We used genetic operation in order to retrieve the information connected between the users by expanding the keywords. Our result shows that the genetic algorithm can be a promising technique in terms of accuracy of the retrieval results.