pyJedAI: A Library with Resolution-Related Structures and Procedures for Products

Abstract

This work presents an open-source Python library, named pyJedAI, 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. History: Accepted by Ted Ralphs, Area Editor for Software Tools. Funding: This was partially funded by the EU project STELAR (Horizon Europe) [Grant 101070122]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0410 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0410 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .