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

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 (Papadakis et al. 2021a), the tool offers a plethora of important tasks required for processing product data collections. It be can easily used by researchers and practitioners for creating algorithms analyzing products, such as real-time ads bidding, sponsored search, or pricing determination. In essence, it allows 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.

Authors

Publication Details

Journal
INFORMS journal on computing
Published
2024-09-09
DOI
https://doi.org/10.1287/ijoc.2023.0410.cd
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
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article

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

George Papadakis, Ekaterini Ioannou, Konstantinos Nikoletos
INFORMS journal on computing
Scientific Computing and Data Management
article

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

George Papadakis, Ekaterini Ioannou, Konstantinos Nikoletos
article en

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 (Papadakis et al. 2021a), the tool offers a plethora of important tasks required for processing product data collections. It be can easily used by researchers and practitioners for creating algorithms analyzing products, such as real-time ads bidding, sponsored search, or pricing determination. In essence, it allows 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.

INFORMS journal on computing
Openalex Percentile: Top 83%
Scientific Computing and Data Management
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Code and Data Repository for pyJedAI: A Library with Resolution-Related Structures and Procedures for Products — George Papadakis, Ekaterini Ioannou, et al. · INFORMS journal on computing (2024) | TGRS Research Map | TGRS