Hyperparameter Optimization for Search Relevance in E-Commerce

Manuel Dalcastagné, Giuseppe Di Fabbrizio

2024

Abstract

The tuning of retrieval and ranking strategies in search engines is traditionally done manually by search experts in a time-consuming and often irreproducible process. A typical use case is field boosting in keyword-based search, where the ranking weights of different document fields are changed in a trial-and-error process to obtain what seems to be the best possible results on a set of manually picked user queries. Hyperparameter optimization (HPO) can automatically tune search engines’ hyperparameters like field boosts and solve these problems. To the best of our knowledge, there has been little work in the research community regarding the application of HPO to search relevance in e-commerce. This work demonstrates the effectiveness of HPO techniques for optimizing the relevance of e-commerce search engines using a real-world dataset and evaluation setup, providing guidelines on key aspects to consider for the application of HPO to search relevance. Differential evolution (DE) optimization achieves up to 13% improvement in terms of NDCG@10 over baseline search configurations on a publicly available dataset.

Paper Citation

in Harvard Style

Dalcastagné M. and Di Fabbrizio G. (2024). Hyperparameter Optimization for Search Relevance in E-Commerce. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 399-407. DOI: 10.5220/0013010500003838

in Bibtex Style

@conference{kdir24,
author={Manuel Dalcastagné and Giuseppe Di Fabbrizio},
title={Hyperparameter Optimization for Search Relevance in E-Commerce},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={399-407},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013010500003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Hyperparameter Optimization for Search Relevance in E-Commerce
SN - 978-989-758-716-0
AU - Dalcastagné M.
AU - Di Fabbrizio G.
PY - 2024
SP - 399
EP - 407
DO - 10.5220/0013010500003838
PB - SciTePress