Estimation of time complexity for the task of retrieval for identical products for an electronic trading platform based on the decomposition of machine learning models

Fedor Krasnov


This paper scrutinize the solution of the problem of matching identical products for an electronic trading platform. This task is one of the components of the recommendation system of the electronic trading platform and belongs to the class of item2item  recommendation systems— product-product recommendations. As part of this task, it is important to find all identical products with different prices and delivery conditions from different suppliers from a product catalog that contains hundreds of millions of products. The mathematical formulation for this problem belongs to the class of NP-complete problems. The author applied a decomposition of machine learning models to solve this problem – faster and more versatile models select candidate pairs of identical products, and then more CPU-intensive and accurate models score the identity of candidate pairs of products. The final model determines the order in a group of identical products based on the ranking model. The result is a list of groups of identical products sorted by the rank of identity within the group. The time complexity metric for the model composition was O(N*N*LOG(P)/M), where N is the total number of products in the catalog, P is the number of product modalities, and M is the number of product categories.

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