Supplier Recommendation in Online Procurement

Fuente: arXiv
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Autori principali: Coscrato, Victor, Bridge, Derek
Natura: Preprint
Pubblicazione: 2024
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author Coscrato, Victor
Bridge, Derek
author_facet Coscrato, Victor
Bridge, Derek
contents Supply chain optimization is key to a healthy and profitable business. Many companies use online procurement systems to agree contracts with suppliers. It is vital that the most competitive suppliers are invited to bid for such contracts. In this work, we propose a recommender system to assist with supplier discovery in road freight online procurement. Our system is able to provide personalized supplier recommendations, taking into account customer needs and preferences. This is a novel application of recommender systems, calling for design choices that fit the unique requirements of online procurement. Our preliminary results, using real-world data, are promising.
format Preprint
id arxiv_https___arxiv_org_abs_2403_01301
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Supplier Recommendation in Online Procurement
Coscrato, Victor
Bridge, Derek
Information Retrieval
Machine Learning
Supply chain optimization is key to a healthy and profitable business. Many companies use online procurement systems to agree contracts with suppliers. It is vital that the most competitive suppliers are invited to bid for such contracts. In this work, we propose a recommender system to assist with supplier discovery in road freight online procurement. Our system is able to provide personalized supplier recommendations, taking into account customer needs and preferences. This is a novel application of recommender systems, calling for design choices that fit the unique requirements of online procurement. Our preliminary results, using real-world data, are promising.
title Supplier Recommendation in Online Procurement
topic Information Retrieval
Machine Learning
url https://arxiv.org/abs/2403.01301