Probabilistic and genetic algorithms in document retrieval
Communications of the ACM
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Tuning before feedback: combining ranking discovery and blind feedback for robust retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Evolving local and global weighting schemes in information retrieval
Information Retrieval
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Voting for candidates: adapting data fusion techniques for an expert search task
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Information Systems
Proximity-based document representation for named entity retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A survey of pre-retrieval query performance predictors
Proceedings of the 17th ACM conference on Information and knowledge management
The Computer Journal
Associating people and documents
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Learning models for ranking aggregates
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Foundations and Trends in Information Retrieval
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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.