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Regular Paper

SMEC: Scene Mining for E-Commerce

State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
JD.com, Inc., Beijing 100176, China
Baidu, Inc., Beijing 100085, China
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Abstract

Scene-based recommendation has proven its usefulness in E-commerce, by recommending commodities based on a given scene. However, scenes are typically unknown in advance, which necessitates scene discovery for E-commerce. In this article, we study scene discovery for E-commerce systems. We first formalize a scene as a set of commodity categories that occur simultaneously and frequently in real-world situations, and model an E-commerce platform as a heterogeneous information network (HIN), whose nodes and links represent different types of objects and different types of relationships between objects, respectively. We then formulate the scene mining problem for E-commerce as an unsupervised learning problem that finds the overlapping clusters of commodity categories in the HIN. To solve the problem, we propose a non-negative matrix factorization based method SMEC (Scene Mining for E-Commerce), and theoretically prove its convergence. Using six real-world E-commerce datasets, we finally conduct an extensive experimental study to evaluate SMEC against 13 other methods, and show that SMEC consistently outperforms its competitors with regard to various evaluation measures.

Keywords

graph clusteringE-commerceheterogeneous information network (HIN)scene mining

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Journal of Computer Science and Technology
Volume 39 Issue 1,
January 2024
Pages 192-210
DOI: 10.1007/s11390-021-1277-0
Cite this article:
Wang G, Li X, Guo Z-Y, et al. SMEC: Scene Mining for E-Commerce. Journal of Computer Science and Technology, 2024, 39(1): 192-210. https://doi.org/10.1007/s11390-021-1277-0

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Received: 08 January 2021
Accepted: 24 October 2021
Published: 25 January 2024
© Institute of Computing Technology, Chinese Academy of Sciences 2024
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