Vu Hong Quan

Vu Hong Quan

Data scientist

Posts by Collection

portfolio

publications

An incremental ensemble learning system for Vietnamese e-commerce product classification

Published in 13th International Conference on Knowledge and Systems Engineering (KSE), 2021

With the booming of e-commerce platforms, text classification models play an increasingly important role in businesses. Major challenges that businesses would face include dataset imbalance, continuously added data, language specificity and product specificity. In this paper, we propose a scalable incremental machine learning system for industrial-scale deployment in real-world business. The system also includes steps to optimize e-commerce product specifics. The proposal tactics including keyword dictionary mapping, sampling technique and ensemble learning delivered better performance when compared to models without them. Our experiments also showed that minibatch SVM produced good results and might be considerable in a lighter system.

Recommended citation: N. T. N. Linh, V. H. Quan, L. H. Ngan, T. D. Phu and H. -Q. Le, "An incremental ensemble learning system for Vietnamese e-commerce product classification," 2021 13th International Conference on Knowledge and Systems Engineering (KSE), 2021, pp. 1-4, doi: 10.1109/KSE53942.2021.9648642. https://doi.org/10.1109/kse53942.2021.9648642

EfficientRec: An unlimited user-item scale recommendation system based on clustering and user’s interaction embedding profile

Published in 14th Asian Conference on Intelligent Information and Database Systems, 2022

Recommendation systems are highly interested in technology companies nowadays. The businesses are constantly growing users and products, causing the number of users and items to continuously increase over time, to very large numbers. Traditional recommendation algorithms with complexity dependent on the number of users and items make them difficult to adapt to the industrial environment. In this paper, we introduce a new method applying graph neural networks with a contrastive learning framework in extracting user preferences. We incorporate a soft clustering architecture that significantly reduces the computational cost of the inference process. Experiments show that the model is able to learn user preferences with low computational cost in both training and prediction phases. At the same time, the model gives a very good accuracy. We call this architecture EfficientRec with the implication of model compactness and the ability to scale to unlimited users and products.

Recommended citation: Quan, V.H., Ngan, L.H., Duc, L.M., Linh, N.T.N., Quynh-Le, H. (2022). EfficientRec: An Unlimited User Scale Recommendation System Based on Clustering and User’s Interaction Embedding Profile. In: Szczerbicki, E., Wojtkiewicz, K., Nguyen, S.V., Pietranik, M., Krótkiewicz, M. (eds) Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2022. Communications in Computer and Information Science, vol 1716. Springer, Singapore. https://doi.org/10.1007/978-981-19-8234-7_53 https://doi.org/10.1007/978-981-19-8234-7_53

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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