Abstract
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.
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References
Abdollahpouri, H., Burke, R., Mobasher, B.: Managing popularity bias in recommender systems with personalized re-ranking (2019)
He, R., McAuley, J.: VBPR: visual Bayesian personalized ranking from implicit feedback. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 144–150. AAAI Press (2016)
Le, K., Nguyen, H., Le Thanh, T., Nguyen, M.: VIMQA: a Vietnamese dataset for advanced reasoning and explainable multi-hop question answering. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 6521–6529, Marseille, France (2022). European Language Resources Association
Li, J., Li, D., Xiong, C., Hoi, S.: Bootstrapping language-image pre-training for unified vision-language understanding and generation, Blip (2022)
Nguyen, D.Q., Nguyen, A.T.: PhoBERT: pre-trained language models for Vietnamese. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 1037–1042 (2020). Association for Computational Linguistics
Ni, J., Li, J., McAuley, J.: Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 188–197, Hong Kong, China (2019). Association for Computational Linguistics
Ouyang, L., et al.: Training language models to follow instructions with human feedback (2022)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_10
Nguyen, P., Tho L.D.: The effect of online product recommendation system on consumer behavior: Vietnamese e-commerce websites 10, 1–24 (2021)
Rashed, A., Elsayed, S., Schmidt-Thieme, L.: Context and attribute-aware sequential recommendation via cross-attention. In: Proceedings of the 16th ACM Conference on Recommender Systems, RecSys 2022, pp. 71–80, New York, NY, USA (2022). Association for Computing Machinery
Robertson, S., Zaragoza, H.: The probabilistic relevance framework: Bm25 and beyond. Found. Trends Inf. Retriev. 3, 333–389 (2009)
Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: Proceedings of the 1st ACM Conference on Electronic Commerce, EC 2099, pp. 158–166, New York, NY, USA (1999). Association for Computing Machinery
Tran, L.Q., Van Duong, B., Nguyen, B.T.: Sentiment classification for beauty-fashion reviews. In: 2022 14th International Conference on Knowledge and Systems Engineering (KSE), pp. 1–6 (2022)
Tran, Q.L., Lam, G.H., Le, Q.N., Tran, T.H., Do, T.H.: A comparison of several approaches for image recognition used in food recommendation system. In: 2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), pp. 284–289 (2021)
Tran, Q.L., Le, P.T. D., Do, T.H.: Aspect-based sentiment analysis for Vietnamese reviews about beauty product on E-commerce websites. In: Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation, pp. 767–776, Manila, Philippines (2022). De La Salle University
Truong, Q.-D., Thi Bui, T.D., Nguyen, H.T.: Product recommendation system using opinion mining on Vietnamese reviews. In: Phuong, N.H., Kreinovich, V. (eds.) Soft Computing: Biomedical and Related Applications. SCI, vol. 981, pp. 313–325. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-76620-7_27
Wu, L., Li, S., Hsieh, C.J., Sharpnack, J.: SSE-PT: sequential recommendation via personalized transformer. In: Proceedings of the 14th ACM Conference on Recommender Systems, RecSys 2020, pp. 328–337, New York, NY, USA (2020). Association for Computing Machinery
Acknowledgements
This research was conducted with the financial support of Science Foundation Ireland at ADAPT, the SFI Research Centre for AI-Driven Digital Content Technology at Dublin City University [13/RC/2106_P2]. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.
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Tran, QL., Nguyen, B.T., Jones, G.J.F., Gurrin, C. (2024). ViEcomRec: A Dataset for Recommendation in Vietnamese E-Commerce. In: HÃ , M.H., Zhu, X., Thai, M.T. (eds) Computational Data and Social Networks. CSoNet 2023. Lecture Notes in Computer Science, vol 14479. Springer, Singapore. https://doi.org/10.1007/978-981-97-0669-3_7
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