@ARTICLE{26583204_934278001_2024, 
	author = {Fedor Krasnov}, 
	keywords = {, embedding-based retrieval, information retrieval, threshold metricssemantic product search},
	title = {<p class="text">Embedding-based retrieval: measures of threshold recall and precision to evaluate product search</p>},
	journal = {},
	year = {2024},
	number = {2 Vol 18},
	pages = {22-34},
	url = {https://bijournal.hse.ru/en/2024--2 Vol 18/934278001.html},
	publisher = {},
	abstract = {&nbsp; &nbsp; &nbsp; Modern product retrieval systems are becoming increasingly complex due to the use of extra product representations, such as user behavior, language semantics and product images. However, adding new information and complicating machine learning models does not necessarily lead to an improvement in online and business search performance, since after retrieval the product list is ranked, which introduces its own bias. Nevertheless, the business performance of a product search will be worse from ranking an incomplete list of products than a complete one, and the relevance of search results will not improve from perfect sorting of products that do not match the search query. Therefore, the main quality indicators for the products retrieval phase remain Recall and Precision at the k threshold. This paper compares several architectures of product retrieval systems in product search for e-commerce. To do this, the concepts of threshold Recall and Precision for information retrieval are investigated and the dependence of these measures on the order of issuance is revealed. An automatic procedure has been developed for calculating R@k and P@k, which allows us to compare the effectiveness of information retrieval systems. The proposed automatic procedure has been tested on the WANDS public dataset for several key architectures. The obtained values R@1000 = 84% &plusmn; 9% and P@10 = 67% &plusmn; 17% are at the level of SOTA models.},
	annote = {&nbsp; &nbsp; &nbsp; Modern product retrieval systems are becoming increasingly complex due to the use of extra product representations, such as user behavior, language semantics and product images. However, adding new information and complicating machine learning models does not necessarily lead to an improvement in online and business search performance, since after retrieval the product list is ranked, which introduces its own bias. Nevertheless, the business performance of a product search will be worse from ranking an incomplete list of products than a complete one, and the relevance of search results will not improve from perfect sorting of products that do not match the search query. Therefore, the main quality indicators for the products retrieval phase remain Recall and Precision at the k threshold. This paper compares several architectures of product retrieval systems in product search for e-commerce. To do this, the concepts of threshold Recall and Precision for information retrieval are investigated and the dependence of these measures on the order of issuance is revealed. An automatic procedure has been developed for calculating R@k and P@k, which allows us to compare the effectiveness of information retrieval systems. The proposed automatic procedure has been tested on the WANDS public dataset for several key architectures. The obtained values R@1000 = 84% &plusmn; 9% and P@10 = 67% &plusmn; 17% are at the level of SOTA models.}
}