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

Author(s)
Rafter, Rachael  
Bradley, Keith  
Smyth, Barry  
Uri
http://hdl.handle.net/10197/4637
Date Issued
1999-09
Date Available
2013-10-01T09:03:43Z
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
Subjects

Artificial Intelligen...

User profiling

Personalised recommen...

Language
English
Status of Item
Not peer reviewed
Conference Details
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/
File(s)
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Name

Rachael-Rafter-CRC.pdf

Size

44.04 KB

Format

Adobe PDF

Checksum (MD5)

aa66efaad213ce66dc9d365a0c788539

Owning collection
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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