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DC FieldValueLanguage
dc.contributorDepartment of Computingen_US
dc.creatorQi, Xianming-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/3949-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleThe implementation of personalized information retrieval in Web-based financial news digest systemen_US
dcterms.abstractIn the survey report "The 22nd Statistical Survey Report on the Internet Development in China" Internet News is the second in the top 10 of the Internet application. A large growing number of Internet users, more and more people pay attention to financial news. People try to reduce the information asymmetric through the Internet. In the area of finance, information is increasing rapidly. The financial news digest system is one kind of financial system; it can provide the effective support and the help for the user. It is quite difficult for Internet users and the majority of financial researchers to accurately obtain financial information on the Internet manually. Especially for financial professionals, we need a professional information retrieval tool to obtain the necessary professional information. But all kinds of Internet finance information as well as more Internet users require custom-made and personalized financial information. It gradually becomes very important in daily application. Nowadays almost all popular free online reader systems just provide a RSS reader service, there is not having a part for the retrieval of ordinary web pages. At the same time there is no system specific to the users who are interested to look at financial news. It is difficult to recommend news by similar of users. In this dissertation, will try to describe how to build an easily and simple system called getlnOne. It only concern with the financial industry, it can support subscription RSS / Atom and the ordinary web pages, find out tags from news and let people modify or confirm. So the system can recommend news through the same tag.en_US
dcterms.extentx, 102 leaves : ill. ; 30 cm.en_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2008en_US
dcterms.educationalLevelAll Masteren_US
dcterms.educationalLevelM.Sc.en_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertations.en_US
dcterms.LCSHJournalism, Commercial -- Data processing.en_US
dcterms.LCSHInformation retrieval.en_US
dcterms.LCSHWeb search engines.en_US
dcterms.LCSHWorld Wide Web -- Subject access.en_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/3949