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Personalised Retrieval for Online Recruitment Services
Author(s)
Date Issued
2000-04-05
Date Available
2013-10-01T09:01:52Z
Abstract
Internet search technology is largely based on exact-match retrieval. Such systems rely on the user to provide an
adequate description of their requirements. However, most users submit poorly specified queries, leading to
imprecise search results. Furthermore, there is no facility for personalising searches to reflect implicit likes and
dislikes of users. We describe two complementary solutions to these problems as implemented in CASPER, an
intelligent online recruitment service. We describe an approach to personalised similarity-based retrieval, and a
queryless collaborative filtering recommendation technique.
adequate description of their requirements. However, most users submit poorly specified queries, leading to
imprecise search results. Furthermore, there is no facility for personalising searches to reflect implicit likes and
dislikes of users. We describe two complementary solutions to these problems as implemented in CASPER, an
intelligent online recruitment service. We describe an approach to personalised similarity-based retrieval, and a
queryless collaborative filtering recommendation technique.
Type of Material
Conference Publication
Language
English
Status of Item
Not peer reviewed
Journal
Proceedings of the 22nd Annual Colloquium on Information Retrieval. 2000.
Conference Details
The BCS/ IRSG 22nd Annual Colloquium on Information Retrieval (IRSG 2000), Cambridge, UK, 5-7 April, 2000
This item is made available under a Creative Commons License
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Name
final_personalisedRetrievalRecruit.pdf
Size
3.22 MB
Format
Adobe PDF
Checksum (MD5)
5dd373099fbb5e278760bf7923b48c94
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