Passive Profiling and Collaborative Recommendation

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Title: Passive Profiling and Collaborative Recommendation
Authors: Rafter, Rachael
Bradley, Keith
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/4637
Date: Sep-1999
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 IntelligenceUser profilingPersonalised recommendations
Language: en
Status of Item: Not peer reviewed
Conference Details: The 10th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 99), Cork, Ireland, September, 1999
Appears in Collections:Computer Science Research Collection

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