An Analysis Framework for Content-based Job Recommendation
|Title:||An Analysis Framework for Content-based Job Recommendation||Authors:||Guo, Xingsheng
O'Mahony, Michael P.
|Permanent link:||http://hdl.handle.net/10197/6107||Date:||29-Sep-2014||Abstract:||In this paper, we focus on the task of job recommendation. In particular, we consider several personalised content-based and case-based approaches to recommendation. We investigate a number of feature-based item representations, along with a variety of feature weighting schemes. A comparative evaluation of the various approaches is performed using a realworld, open source dataset.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Copyright (published version):||2014 the Author||Keywords:||Recommender Systems; Open data||Other versions:||http://www.iccbr.org/iccbr14/||Language:||en||Status of Item:||Peer reviewed||Conference Details:||22nd International Conference on Case-Based Reasoning (ICCBR), Cork, Ireland, 29 September - 01 October 2014|
|Appears in Collections:||Insight Research Collection|
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