Development of an Intelligent Recommendation System for Cross-Border E-commerce Platforms using Data Mining Techniques

DOI

10.2991/978-94-6463-326-9_36How to use a DOI?

Keywords

Data Mining Techniques; Intelligent Recommendation System; Recommendation System Modeling

Abstract

As cross-border e-commerce grows, intelligent recommendation systems are crucial for platform competitiveness. This research aims to leverage data mining techniques to provide personalized, accurate recommendations. It outlines using data mining in recommendations, and proposes a development process including data preprocessing, feature engineering, model training, and system implementation with a Spark streaming architecture. Key modules are designed for data processing, model training, and online recommendations. Evaluation metrics are established to test performance. The research shows data mining techniques can effectively uncover user interests and patterns for personalized intelligent recommendations. It provides insights into designing cross-border e-commerce recommendation systems. In summary, this research demonstrates data mining techniques can enable effective personalized recommendations, with an outlined development process and insights for system design.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hui Zhang
PY  - 2023
DA  - 2023/12/30
TI  - Development of an Intelligent Recommendation System for Cross-Border E-commerce Platforms using Data Mining Techniques
BT  - Proceedings of the 2023 3rd International Conference on Business Administration and Data Science (BADS 2023)
PB  - Atlantis Press
SP  - 351
EP  - 358
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-326-9_36
DO  - 10.2991/978-94-6463-326-9_36
ID  - Zhang2023
ER  -