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Authors: André Gomes Regino 1 ; Rodrigo Oliveira Caus 1 ; Victor Hochgreb 1 and Julio Cesar Dos Reis 2

Affiliations: 1 Institute of Computing, University of Campinas, Campinas, São Paulo, Brazil ; 2 GoBots, Campinas, São Paulo, Brazil

Keyword(s): Recommendation Systems, Knowledge Graphs, Question Answering Systems.

Abstract: The amount of data generated in e-commerce sales has expressively grown in the last few years. Online stores often receive questions about products related to price, guarantee, and shipping price. By reducing time for prompt answering, stores can improve customer satisfaction and sales conversion rate. The recommendation of available alternative products in case of product unavailability intended by the customer plays a key role in sales growth in this context. This article defines and evaluates a technique for product recommendation based on the product’s facts stored in Knowledge Graphs (KGs). Our KG is filled with facts from natural language questions and answers processed from the e-commerce platform. We exemplify our proposal in a real-world solution, using data from online stores processed by GoBots, a leading e-commerce chatbot business in Latin America. Online sellers assessed the results of the recommendations to evaluate their quality.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Regino, A. G., Caus, R. O., Hochgreb, V. and Reis, J. C. (2022). Knowledge Graph-based Product Recommendations on e-Commerce Platforms. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KEOD; ISBN 978-989-758-614-9; ISSN 2184-3228, SciTePress, pages 32-42. DOI: 10.5220/0011388300003335

@conference{keod22,
author={André Gomes Regino and Rodrigo Oliveira Caus and Victor Hochgreb and Julio Cesar Dos Reis},
title={Knowledge Graph-based Product Recommendations on e-Commerce Platforms},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KEOD},
year={2022},
pages={32-42},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011388300003335},
isbn={978-989-758-614-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KEOD
TI - Knowledge Graph-based Product Recommendations on e-Commerce Platforms
SN - 978-989-758-614-9
IS - 2184-3228
AU - Regino, A.
AU - Caus, R.
AU - Hochgreb, V.
AU - Reis, J.
PY - 2022
SP - 32
EP - 42
DO - 10.5220/0011388300003335
PB - SciTePress

- Science and Technology Publications, Lda.
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