@article{Wang2024, 
author = {Gang Wang and Xiang Li and Zi-Yi Guo and Da-Wei Yin and Shuai Ma},
title = {SMEC: Scene Mining for E-Commerce},
year = {2024},
journal = {Journal of Computer Science and Technology},
volume = {39},
number = {1},
pages = {192-210},
keywords = {graph clustering, E-commerce, heterogeneous information network (HIN), scene mining},
url = {https://www.sciopen.com/article/10.1007/s11390-021-1277-0},
doi = {10.1007/s11390-021-1277-0},
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.}
}