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Ranking with Genetics: Improving Document Ranking With Genetic and Optimization Algorithms

Ranking with Genetics: Improving Document Ranking With Genetic and Optimization Algorithms

Lawrence Master
Copyright: © 2020 |Volume: 10 |Issue: 3 |Pages: 15
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781799807339|DOI: 10.4018/IJIRR.2020070102
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MLA

Master, Lawrence. "Ranking with Genetics: Improving Document Ranking With Genetic and Optimization Algorithms." IJIRR vol.10, no.3 2020: pp.20-34. http://doi.org/10.4018/IJIRR.2020070102

APA

Master, L. (2020). Ranking with Genetics: Improving Document Ranking With Genetic and Optimization Algorithms. International Journal of Information Retrieval Research (IJIRR), 10(3), 20-34. http://doi.org/10.4018/IJIRR.2020070102

Chicago

Master, Lawrence. "Ranking with Genetics: Improving Document Ranking With Genetic and Optimization Algorithms," International Journal of Information Retrieval Research (IJIRR) 10, no.3: 20-34. http://doi.org/10.4018/IJIRR.2020070102

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Abstract

There are many applications for ranking, including page searching, question answering, recommender systems, sentiment analysis, and collaborative filtering, to name a few. In the past several years, machine learning and information retrieval techniques have been used to develop ranking algorithms and several list wise approaches to learning to rank have been developed. We propose a new method, which we call GeneticListMLE++ and GeneticListNet++, which build on the original ListMLE and ListNet algorithms. Our method substantially improves on the original ListMLE and ListNet ranking approaches by incorporating genetic optimization of hyperparameters, a nonlinear neural network ranking model, and a regularization technique.

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