Passive Profiling and Collaborative Recommendation
|Title:||Passive Profiling and Collaborative Recommendation||Authors:||Rafter, Rachael
|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 Intelligence;User profiling;Personalised recommendations||Language:||en||Status of Item:||Not peer reviewed||Conference Details:||The 10th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 99), Cork, Ireland, September, 1999|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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