Clusterization methods for multi-variant e-commerce interfaces

• Autorzy: Wasilewski Adam

Identyfikatory

DOI

10.15439/2023F1377

• Języki publikacji: EN

Abstrakty

EN

E-commerce is a very popular method that let consumers to purchase goods and services. The ability to purchase items online has increased the need for effective recommendation systems. Such recommendations relate usually to products in which the customer may be interested. However, there are wider opportunities to tailor e-commerce to individual customer needs and behaviour. In this paper the architecture of the ecommerce platform (named AIM2), which allows to provide a dedicated interface to selected user groups is discussed. A key component of the platform is the module responsible for dividing customers into groups, using selected clustering methods. Each of the implemented methods can be parameterised to adapt the customer segmentation to the requirements of the e-commerce owner. This article describes the results of an analysis of the impact of selected methods and parameters on clustering results. Moreover, it identifies key metrics that should be considered when selecting clustering conditions during the implementation of the platform. Finally, the main results of the pilot implementation of AIM2 are presented to assess the effectiveness of the multivariant user interface.

Słowa kluczowe

EN

measurement; computer science; user interfaces; electronic commerce; recommender system

PL

pomiar; informatyka; interfejs użytkownika; handel elektroniczny; system rekomendacji

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 309--313
• Opis fizyczny: Bibliogr. 23 poz.

Twórcy

autor

Wasilewski Adam

• Wroclaw University of Science and Technology Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland Fast White Cat S.A., Poland

• https://orcid.org/0000-0002-1653-5005

Bibliografia

• 1. J. Ah-Pine, “An Efficient and Effective Generic Agglomerative Hierarchical Clustering Approach”. Journal of Machine Learning Research vol 19(1), 2018, pp. 1615-1658.
• 2. F. Andriyani, Y. Puspitarani “Performance Comparison of K-Means and DBScan Algorithms for Text Clustering Product Reviews”, SinkrOn vol. 7(3), 2022, pp. 944-949.
• 3. L. Ardissono, A. Goy, G. Petrone, M. Segnan “A multi-agent infrastructure for developing personalized web-based systems” ACM Transactions on Internet Technology vol. 5(1), 2005, pp. 47-69.
• 4. D. Arthur, S. Vassilvitskii “k-means++: the advantages of careful seeding”, SODA ’07: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 2007, pp. 1027-1035.
• 5. R.P. Chatterjee, K. Deb, S. Banerjee, A. Das, R. Bag “Web Mining Using K-Means Clustering and Latest Substring Association Rule for E-Commerce”, Journal of Mechanics of Continua and Mathematical Sciences vol. 14(6), 2019, pp. 28-44.
• 6. X. Chen, W. Sun, B. Wang, Z. Li, X. Wang, Y. Ye “Spectral Clustering of Customer Transaction Data With a Two-Level Subspace Weighting Method” IEEE Transactions on Cybernetics vol. 49, 2019, pp. 3230.
• 7. Darwin, R. Purba, M.F. Pasha “Search Query Clustering Comparation On E-Commerce Using K-Means And Adaptive DBSCAN” 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT), 2020, pp. 207-211.
• 8. M. Ester, H.-P. Kriegel, J. Sander, X. Xu “A density-based algorithm for discovering clusters in large spatial databases with noise”, KDD’96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996, pp. 226-231.
• 9. F. Gözükara, S.A. Özel “An Incremental Hierarchical Clustering Based System For Record Linkage In E-Commerce Domain” The Computer Journal vol. 66(3), 2021, pp. 581-602.
• 10. P.D.Hung, N.T.T. Lien, N.D. Ngoc “Customer Segmentation Using Hierarchical Agglomerative Clustering” ICISS ’19: Proceedings of the 2nd International Conference on Information Science and Systems, 2019, pp. 33-37.
• 11. P. Jiang, Y. Zhu, Y. Zhang, Q. Yuan “Life-stage Prediction for Product Recommendation in E-commerce” KDD ’15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 1879-1888.
• 12. A. Kobsa, “Personalized hypermedia and international privacy” Communications of the ACM vol. 45, 2002, pp. 64-67.
• 13. R.V.S. Kumar, S.S. Rao, P. Srinivasrao “An Efficient Clustering Approach using DBSCAN” Helix vol. 8(3), 2018, pp. 3399-2305.
• 14. L. McInnes, J. Healy, J. Melville “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction” ArXiv e-prints, 2020, https://arxiv.org/pdf/1802.03426
• 15. M. Montaner, B. López, J. L. de la Rosa “A Taxonomy of Recommender Agents on the Internet” Artificial Intelligence Review vol. 19, 2003, pp. 285-330.
• 16. E. Patel, D.S. Kushwaha “Clustering Cloud Workloads: K-Means vs Gaussian Mixture Model” Artificial Procedia Computer Science vol. 171, 2020, pp. 158-167.
• 17. K. Tabianan, S. Velu, V. Ravi “K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data”, Sustainability vol. 14(12), 2022, pp. 1-15.
• 18. E. Triandini, F.A. Hermawati, I.K.P. Suniantara “Hierarchical Clustering for Functionalities E-Commerce Adoption”, Jurnal Ilmiah KURSOR vol. 10(3), 2020, pp. 111-118.
• 19. Y. Yang, J. Jiang, H. Wang “Application of E-Commerce Sites Evaluation Based on Factor Analysis and Improved DBSCAN Algorithm”, International Conference on Management of e-Commerce and e-Government, 2018, pp. 33-38.
• 20. L. Wang, Y. Jing “Collocating Recommendation Method for E-Commerce Based on Fuzzy C-Means Clustering Algorithm”, Journal of Mathematics vol. 2022, 2022, pp. 1-11.
• 21. Z. Wu, L. Jin, J. Zhao, L. Jing, L. Chen “Research on Segmenting E-Commerce Customer through an Improved K-Medoids Clustering Algorithm”, Computational Intelligence and Neuroscience vol. 2022, 2022, pp. 1-10.
• 22. B. Zhang, L. Wang, Y. Li “Precision Marketing Method of E-Commerce Platform Based on Clustering Algorithm”, Complexity vol. 2021, 2021, pp. 1-10.
• 23. Y. Zhang, M. Li, S. Wang, S. Dai, L. Luo, E. Zhu, H. Xu, X, Zhu, C. Yao, H. Zhou “Gaussian Mixture Model Clustering with Incomplete Data”, ACM Transactions on Multimedia Computing, Communications, and Applications vol. 17(1s), 2021, pp. 1-14.

Uwagi

1. Project "Self-adaptation of the online store interface for the customer requirements and behaviour" co-funded by the National Centre for Research and Development under the Sub-Action 1.1.1 of the Operational Programme Intelligent Development 2014-2020.

2. The datasets used during the study are available from the author upon reasonable request.

3. Main Track Short Papers

4. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).