Learning Aggregation Functions for Expert Search

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...

• Type: Conference
• Year: 2010
• Where: ECAI
• Authors: Ronan Cummins, Mounia Lalmas, Colm O'Riordan
• Keywords: Artificial Intelligence, ECAI 2010, Expert Search, Expert Search Approach, Information