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New Technology of Library and Information Service  2010, Vol. 26 Issue (1): 22-27    DOI: 10.11925/infotech.1003-3513.2010.01.05
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An Intelligent Commodity Information Retrieval Based on Semantic Similarity and Multi-attribute Decision Method
Zeng Ziming   Zhang Liyi
(Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China)
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In this paper,a commodity information retrieval model is presented, which integrates semantic retrieval and multi-attribute decision method. Firstly, semantic similarity is computed by constructing semantic vector-space in order to realize the semantic consistency between retrieved result and customer’s query. Besides, TOPSIS method is also utilized to construct the comparison mechanism of commodity by calculating the utility value of each retrieved commodity. Finally, the experiment is conducted in terms of accuracy and customer acceptance rate, and the results verify the effectiveness of the model,which can improve the precision of the commodity information retrieval.

Key wordsSemantic vector      Multi-attribute decision      Commodity information retrieval     
Received: 21 December 2009      Published: 25 January 2001


Corresponding Authors: Zeng Ziming     E-mail:
About author:: Zeng Ziming,Zhang Liyi

Cite this article:

Zeng Ziming,Zhang Liyi. An Intelligent Commodity Information Retrieval Based on Semantic Similarity and Multi-attribute Decision Method. New Technology of Library and Information Service, 2010, 26(1): 22-27.

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