The Semantic Searching Algorithm Driven by Ecommerce Information Model

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 takes advantage of 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 can 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