A Study and Design of Web Information Search Model Based on Mobile Agent

2010 ◽  
Author(s):  
Sun Yuyu ◽  
Hu Liang ◽  
Zhao Kuo
Keyword(s):  
Mobile Agent ◽  
Information Search ◽  
Search Model ◽  
Model Based ◽  
Web Information

A Lexical Search Model based on word association norms

2019 ◽  
Vol 36 (5) ◽  
pp. 4587-4597
Author(s):  
Jorge Reyes-Magaña ◽  
Gemma Bel-Enguix ◽  
Helena Gómez-Adorno ◽  
Gerardo Sierra
Keyword(s):  
Word Association ◽  
Search Model ◽  
Model Based

Realization of an Intelligent E-Commerce Model Based on Mobile Agent

2014 ◽  
Vol 543-547 ◽  
pp. 4198-4201
Author(s):  
Xiao Guang Li ◽  
Zhan Jun Gao
Keyword(s):  
Mobile Agent ◽  
Mobile Agents ◽  
Simulation Experiment ◽  
The Internet ◽  
Variable Time ◽  
Model Based

Mobile agent is one of the most prominent technologies believed to be playing an important role in future e-commerce. After presented an intelligent e-commerce model based on OBI ( open buying on the internet) , we developed a modified approach for the security of mobile agents and e-commerce, and designed an intelligent shopping algorithm based on variable time negotiation function. The presented model has been evaluated by simulation experiment. It has been found that the presented model is efficient.

scholarly journals Query Recommendation Using Hybrid Query Relevance

2018 ◽  
Vol 10 (11) ◽  
pp. 112
Author(s):  
Jialu Xu ◽  
Feiyue Ye
Keyword(s):  
Information Retrieval ◽  
Information Search ◽  
Search Engines ◽  
Web Search ◽  
Superior Performance ◽  
Recommendation Algorithm ◽  
Web Information ◽  
Query Recommendation

With the explosion of web information, search engines have become main tools in information retrieval. However, most queries submitted in web search are ambiguous and multifaceted. Understanding the queries and mining query intention is critical for search engines. In this paper, we present a novel query recommendation algorithm by combining query information and URL information which can get wide and accurate query relevance. The calculation of query relevance is based on query information by query co-concurrence and query embedding vector. Adding the ranking to query-URL pairs can calculate the strength between query and URL more precisely. Empirical experiments are performed based on AOL log. The results demonstrate the effectiveness of our proposed query recommendation algorithm, which achieves superior performance compared to other algorithms.

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