THE ECOMMERCE INFORMATION MODEL DRIVEN SEMANTIC SEARCHING ALGORITHM
To make ECommerce information searching across Internet more efficient, ECommerce information searching becomes more and more important. In this paper, ECommerce Information Model (EIM) and a novel EIM-based semantic similarity algorithm are presented. This semantic similarity algorithm utilizes ECommerce-based information content and edge-based distance in calculating conceptual similarity.According to EIM, a semantic eigenvector, which consists of the semantic similarity values of a given document, is used to represent the semantic content of the document. The semantic eigenvectors and EIM-based similarity function could be applied to ECommerce information retrieval.Experimental results show that the performance of the proposed method is much improved when compared with that of the traditional Information retrieval techniques.
Semantic Search Similarity ECommerce Information content
Yun Ling Ouyang Yi Biwei Li
College of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou 310035
国际会议
杭州
英文
840-844
2006-10-12（万方平台首次上网日期，不代表论文的发表时间）