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  5. Passive Profiling and Collaborative Recommendation
 
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Passive Profiling and Collaborative Recommendation

File(s)
FileDescriptionSizeFormat
Download Rachael-Rafter-CRC.pdf44.04 KB
Author(s)
Rafter, Rachael 
Bradley, Keith 
Smyth, Barry 
Uri
http://hdl.handle.net/10197/4637
Date Issued
September 1999
Date Available
01T09:03:43Z October 2013
Abstract
Online recruitment services have rapidly become one of the most popular types of application on the World-Wide Web. The award-winning JobFinder site ( www.jobfinder.ie) is good example. It allows users to browse and search a large database of current jobs from a variety of employment sectors. However, like many similar Internet applications JobFinder has a number of shortcomings, due mainly to its reliance on traditional database technology and client-pull information access models, which place the burden of navigation and search on the user. To address these shortcomings the CASPER (Case-based Agency: Skill Profiling & Electronic Recruitment) project seeks to investigate the role of Artificial Intelligence techniques such as Automated Collaborative Filtering (ACF) and Case-Based Reasoning (CBR) as a means of providing a more proactive, personalised and intelligent model of information access.
Type of Material
Conference Publication
Keywords
  • Artificial Intelligen...

  • User profiling

  • Personalised recommen...

Language
English
Status of Item
Not peer reviewed
Description
The 10th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 99), Cork, Ireland, September, 1999
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Computer Science Research Collection
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1474
Acquisition Date
Feb 5, 2023
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Acquisition Date
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