Search Engine Based on Semantic Similarity and Multi-Attribute Decision Method

  • 更新时间: 2017-09-14
  • 作者: Ziming Zeng
  • 浏览数: 62
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【摘 要】 The paper presented an intelligent commodity information search model, which integrates seman tic retrieval and multi-attribute decision method. First, 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 and it can improve the precision of the commodity information search.

【发布时间】 2010-07-01

【发布位置】 Center for Studies of Information Resources, Wuhan Univers

标签: Semantic vector Multi-attribute decision Commodity information

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