Rigid or Flexible? A New Navigation Approach for Better Consumer Service Based on Knowledge Enhancement
Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.26
With the rapid development of the Internet and E-commerce, online shopping sites are becoming a popular platform for products selling. Shopping sites such as amazon.com, dangdang.com provide consumers with a hierarchical navigation for selecting products easily from overwhelming amount of products. However, those man-made navigations are so general and professional that consumers still need to spend much time in filtering out their own undesired products personally. Shopping sites provide abundant textual product descriptions for most products, which describes the details of the product. In this paper, we propose a novel model to build a topic hierarchy from the detailed product descriptions, which can automatically model words into a tree structure by hierarchical Latent Dirichlet Allocation (hLDA), besides, our model can also augment words level allocations with the conceptual relation between words in WordNet automatically. Each node in the hierarchical tree contains some relevant keywords of product descriptions, thus clarifying the meaning of the concept in the node. Therefore, consumers can pick out their interested products by using the discovered descriptive and valuable navigation of products. The experimental results on amazon.com, one of the most popular shopping sites in America, demonstrate the efficiency and effectiveness of our proposed model.
Resource management, Navigation, Knowledge based systems, Books, Buildings, Vectors, Software, level allocations, Shopping site, hierarchical Latent Dirichlet Allocation, conceptual relation
Hongyun Bao, Qiudan Li, Daniel Zeng, Heng Gao, "Rigid or Flexible? A New Navigation Approach for Better Consumer Service Based on Knowledge Enhancement", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 280-286, doi:10.1109/ICDMW.2012.26