py J ed AI: A Library with Resolution-Related Structures and Procedures for Products

Ekaterini Ioannou (), Konstantinos Nikoletos () and George Papadakis ()
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Ekaterini Ioannou: Tilburg University, 5037 AB Tilburg, Netherlands
Konstantinos Nikoletos: National and Kapodistrian University of Athens, Athens 157 72, Greece
George Papadakis: National and Kapodistrian University of Athens, Athens 157 72, Greece

INFORMS Journal on Computing, 2025, vol. 37, issue 3, 516-530

Abstract: This work presents an open-source Python library, named py J ed AI, which provides functionalities supporting the creation of algorithms related to product entity resolution. Building over existing state-of-the-art resolution algorithms, the tool offers a plethora of important tasks required for processing product data collections. It can be easily used by researchers and practitioners for creating algorithms analyzing products, such as real-time ad bidding, sponsored search, or pricing determination. In essence, it allows users to easily import product data from the possible sources, compare products in order to detect either similar or identical products, generate a graph representation using the products and desired relationships, and either visualize or export the outcome in various forms. Our experimental evaluation on data from well-known online retailers illustrates high accuracy and low execution time for the supported tasks. To the best of our knowledge, this is the first Python package to focus on product entities and provide this range of product entity resolution functionalities.

Keywords: product entity resolution; integration; similarity; resolution open-source software; product-related structures/procedures (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:37:y:2025:i:3:p:516-530

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