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ViEcomRec: a dataset for recommendation in Vietnamese E-commerce

Tran, Quang-Linh orcid logoORCID: 0000-0002-5409-0916, Nguyen, Binh T. orcid logoORCID: 0000-0001-5249-9702, Jones, Gareth J. F. orcid logoORCID: 0000-0003-2923-8365 and Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 (2024) ViEcomRec: a dataset for recommendation in Vietnamese E-commerce. In: 12th International Conference on Computational Data and Social Networks. CSoNet 2023, 11-13 Dec 2023, Hanoi, Vietnam. ISBN 978-981-97-0668-6

Recent years have seen the increasing popularity of e-commerce platforms which have changed the shopping behaviour of customers. Valuable data from products, customers, and purchases on such e-commerce platforms enable the delivery of personalized shopping experiences, customer targeting, and product recommendations. We introduce a novel Vietnamese dataset specifically designed to examine the recommendation problem in e-commerce platforms, focusing on face cleanser products with 369,099 interactions between users and items. We report a comprehensive baseline experimental exploration into this dataset from content-based filtering to attribute-based filtering approaches. The experimental results demonstrate an enhancement in performance, with a 27.21% improvement in NDCG@10 achieved by incorporating a popularity score and content-based filtering, surpassing attribute-based filtering. To encourage further research and development in e-commerce recommendation systems using this Vietnamese dataset, we have made the dataset publicly available at https://github.com/linh222/face_cleanser_recommendation_dataset.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:Vietnamese datasets; e-commerce recommendation; content-based filtering
Subjects:Computer Science > Artificial intelligence
Computer Science > Information retrieval
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Published in: Computational Data and Social Networks: CSoNet 2023:. Lecture Notes in Computer Science 14479. Springer. ISBN 978-981-97-0668-6
Official URL:https://doi.org/10.1007/978-981-97-0669-3_7
Copyright Information:© 2022 The Authors
Funders:Science Foundation Ireland at ADAPT, the SFI Research Centre for AI-Driven Digital Content Technology at Dublin City University [13/RC/2106 P2]
ID Code:29693
Deposited On:07 Mar 2024 14:19 by Linh Tran . Last Modified 07 Mar 2024 14:19

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