- author = "Ronan Cummins and Mounia Lalmas and Colm O'Riordan",
- title = "Learning Aggregation Functions for Expert Search",
- year = "2010",
- booktitle = "Proceedings of the 19th European Conference on Artificial Intelligence, ECAI 2010",
- editor = "Helder Coelho and Rudi Studer and Michael Wooldridge",
- volume = "215",
- series = "Frontiers in Artificial Intelligence and Applications",
- pages = "535--540",
- address = "Lisbon, Portugal",
- month = aug # " 16-20",
- publisher = "IOS Press",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-1-60750-605-8",
- annote = "The Pennsylvania State University CiteSeerX Archives",
- bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
- bibsource = "DBLP, http://dblp.uni-trier.de",
- language = "en",
- oai = "oai:CiteSeerX.psu:10.1.1.419.500",
-
URL = "
http://ebooks.iospress.nl/publication/5831",
-
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.419.500",
-
URL = "
http://ir.dcs.gla.ac.uk/~mounia/Papers/ECAI10.pdf",
-
URL = "http://www.booksonline.iospress.nl/Content/View.aspx?piid=17702",
-
URL = "http://dx.doi.org/10.3233/978-1-60750-606-5-535",
- size = "6 pages",
-
abstract = "Machine learning techniques are increasingly being
applied to problems in the domain of information
retrieval and text mining. In this paper we present an
application of evolutionary computation to the area of
expert search. Expert search in the context of
enterprise information systems deals with the problem
of finding and ranking candidate experts given an
information need (query). A difficult problem in the
area of expert search is finding relevant information
given an information need and associating that
information with a potential expert.
We attempt to improve the effectiveness of a benchmark expert search approach by adopting a learning model (genetic programming) that learns how to aggregate the documents/information associated with each expert. In particular, we perform an analysis of the aggregation of document information and show that different numbers of documents should be aggregated for different queries in order to achieve optimal performance.
We then attempt to learn a function that optimises the effectiveness of an expert search system by aggregating different numbers of documents for different queries. Furthermore, we also present experiments for an approach that aims to learn the best way to aggregate documents for individual experts. We find that substantial improvements in performance can be achieved, over standard analytical benchmarks, by the latter of these approaches.",
- notes = "ECAI",
