We use cookies to improve your experience with our site.
Gang Wang, Xiang Li, Zi-Yi Guo, Da-Wei Yin, Shuai Ma. SMEC: Scene Mining for E-Commerce[J]. Journal of Computer Science and Technology. DOI: 10.1007/s11390-021-1277-0
Citation: Gang Wang, Xiang Li, Zi-Yi Guo, Da-Wei Yin, Shuai Ma. SMEC: Scene Mining for E-Commerce[J]. Journal of Computer Science and Technology. DOI: 10.1007/s11390-021-1277-0

SMEC: Scene Mining for E-Commerce

  • 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 its 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 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 6 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.
  • loading

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return