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dc.contributor.authorAfrin, Fahmida
dc.contributor.authorNahar, Irin
dc.date.accessioned2015-06-01T10:18:27Z
dc.date.available2015-06-01T10:18:27Z
dc.date.issued2015
dc.identifier.otherID 14101269
dc.identifier.otherID 10201026
dc.identifier.urihttp://hdl.handle.net/10361/4177
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2015.en_US
dc.description.abstractIn today’s world of smart phones and smart machines, our job search systems are still not smart enough. With the increasing population, and the rapid growth of internet usage, online job search is not a new issue anymore. From our very own bdjobs.com to the recent loosemonkies.com, there are many online based job sites. The sites still rely on the Boolean matching, whereas they have large amount of data of job seekers and recruiters. Our goal is to use this CV, Job and recruitment information to train the job search system incrementally, exactly the way, human beings learn. With every successful recruitment, and with every failed approach, the job search system will learn to predict, learn to behave how a recruiter does while recruiting an employee. Thus, not only the job seekers, the recruiters can rely on the job search system too for the best selection of employees.en_US
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.subjectComputer science and engineeringen_US
dc.titleIncremental learning based intelligent job search systemen_US
dc.typeThesisen_US


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