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Collaborative personalization on medical search engines using user exploratory survey

  • School of Information Technology
  • Monash University Malaysia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The surge of medical search engines in the web space is irrepressible. Medical search engines contain concise, structured and accurate information. As a result, medical search engines do not return cornucopia of search results to users. Search results returned by medical search engines are limited but relevant to a users query. Albeit a newcomer to the web, medical search engines are quickly gaining popularity among web users. To date, vertical search engines have not employed collaborative personalization techniques. Current techniques focus on assisting users with query manipulation. This research study identifies a new way of forming collaborative groups based on the user's information seeking goal, behavior and testing its efficacy using user exploratory survey. It also asses' effectiveness of current query manipulation techniques on medical search engines. By doing so, the practical implication is that this will lead to improved search effectiveness through enhanced personalization.

Original languageEnglish
Title of host publicationProceedings - 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010
PublisherIEEE Computer Society
Pages2786-2790
Number of pages5
ISBN (Print)9781424459346
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010 - Yantai, Shandong, China
Duration: 10 Aug 2010 → 12 Aug 2010

Publication series

NameProceedings - 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010
Volume6

Conference

Conference7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010
Country/TerritoryChina
CityYantai, Shandong
Period10/08/10 → 12/08/10

Keywords

  • Collaboration
  • Exploratory research
  • Medical search engines
  • Personalization

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