A Commodity Information Search Model of E-Commerce Search Engine Based on Semantic Similarity and Multi-Attribute Decision Method

  •  Ziming Zeng    


The paper presented an intelligent commodity information search model, which integrates semantic 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.

This work is licensed under a Creative Commons Attribution 4.0 License.