scholarly journals Intelligent Information Retrieval Using Hybrid of Fuzzy Set and Trust

2017 ◽  
Vol 10 (2) ◽  
pp. 311-325
Author(s):  
Suruchi Chawla
Keyword(s):  
Information Retrieval ◽  
Fuzzy Set ◽  
Query Expansion ◽  
Information Need ◽  
Data Set ◽  
Search Results ◽  
Intelligent Information Retrieval ◽  
Main Challenge ◽  
Fuzzy Query ◽  
Intelligent Information

The main challenge for effective web Information Retrieval(IR) is to infer the information need from user’s query and retrieve relevant documents. The precision of search results is low due to vague and imprecise user queries and hence could not retrieve sufficient relevant documents. Fuzzy set based query expansion deals with imprecise and vague queries for inferring user’s information need. Trust based web page recommendations retrieve search results according to the user’s information need. In this paper an algorithm is designed for Intelligent Information Retrieval using hybrid of Fuzzy set and Trust in web query session mining to perform Fuzzy query expansion for inferring user’s information need and trust is used for recommendation of web pages according to the user’s information need. Experiment was performed on the data set collected in domains Academics, Entertainment and Sports and search results confirm the improvement of precision.

Web Page Recommender System using hybrid of Genetic Algorithm and Trust for Personalized Web Search

2018 ◽  
Vol 11 (2) ◽  
pp. 110-127 ◽  
Author(s):  
Suruchi Chawla
Keyword(s):  
Genetic Algorithm ◽  
Web Search ◽  
Web Pages ◽  
Information Need ◽  
Web Page ◽  
Data Set ◽  
Search Results ◽  
Page Ranking ◽  
Main Challenge ◽  
Optimal Ranking

The main challenge to effective information retrieval is to optimize the page ranking in order to retrieve relevant documents for user queries. In this article, a method is proposed which uses hybrid of genetic algorithms (GA) and trust for generating the optimal ranking of trusted clicked URLs for web page recommendations. The trusted web pages are selected based on clustered query sessions for GA based optimal ranking in order to retrieve more relevant documents up in ranking and improves the precision of search results. Thus, the optimal ranking of trusted clicked URLs recommends relevant documents to web users for their search goal and satisfy the information need of the user effectively. The experiment was conducted on a data set captured in three domains, academics, entertainment and sports, to evaluate the performance of GA based optimal ranking (with/without trust) and search results confirms the improvement of precision of search results.

Web Page Recommender System using hybrid of Genetic Algorithm and Trust for Personalized Web Search

2021 ◽  
pp. 656-675
Author(s):  
Suruchi Chawla
Keyword(s):  
Genetic Algorithm ◽  
Web Search ◽  
Web Pages ◽  
Information Need ◽  
Web Page ◽  
Data Set ◽  
Search Results ◽  
Page Ranking ◽  
Main Challenge ◽  
Optimal Ranking

The main challenge to effective information retrieval is to optimize the page ranking in order to retrieve relevant documents for user queries. In this article, a method is proposed which uses hybrid of genetic algorithms (GA) and trust for generating the optimal ranking of trusted clicked URLs for web page recommendations. The trusted web pages are selected based on clustered query sessions for GA based optimal ranking in order to retrieve more relevant documents up in ranking and improves the precision of search results. Thus, the optimal ranking of trusted clicked URLs recommends relevant documents to web users for their search goal and satisfy the information need of the user effectively. The experiment was conducted on a data set captured in three domains, academics, entertainment and sports, to evaluate the performance of GA based optimal ranking (with/without trust) and search results confirms the improvement of precision of search results.

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