Improve the Search and Ranking with Neural Networks

2013 ◽  
Vol 441 ◽  
pp. 721-726
Author(s):  
Yu Wen Chen ◽  
Ju Zhang ◽  
Kun Hua Zhong ◽  
Lei Feng Liu ◽  
Yuan Yao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Search Engine ◽  
Full Text ◽  
Retrieval System ◽  
User Behavior ◽  
Text Retrieval ◽  
Bp Neural Networks ◽  
Ranking System ◽  
Full Text Retrieval

The full text retrieval system can receive constant feedback in the form of user behavior. In the case of a search engine, each user will immediately provide information about how much he likes the results for a given search by clicking on one result and choosing not to click on the others. This paper will look at a way to record when a user clicks on a result after a query, and design a Click-Tracking Network. Then training it with BP neural networks to intelligently improve the rankings of the results for users. Finally, we implement a search and ranking system content-based ranking and improve the search and ranking with neural network. By experiments we have shown good results.

Adding compression to a full-text retrieval system

1995 ◽  
Vol 25 (8) ◽  
pp. 891-903 ◽  
Author(s):  
Justin Zobel ◽  
Alistair Moffat
Keyword(s):  
Full Text ◽  
Retrieval System ◽  
Text Retrieval ◽  
Full Text Retrieval

scholarly journals A Full Text Retrieval System in a Digital Library Environment

2016 ◽  
Vol 08 (01) ◽  
pp. 1-8 ◽  
Author(s):  
Kehinde Daniel Aruleba ◽  
Dipo Theophilus Akomolafe ◽  
Babajide Afeni
Keyword(s):  
Digital Library ◽  
Full Text ◽  
Retrieval System ◽  
Text Retrieval ◽  
Full Text Retrieval

scholarly journals A Multilingual Full-text Retrieval System for Tagged Documents

1999 ◽  
Vol 7 (0) ◽  
pp. 49-52
Author(s):  
Tetsuo Sakaguchi ◽  
Shigetaka Nakao ◽  
Akira Maeda ◽  
Shieo Sugimoto ◽  
Koichi Tabata
Keyword(s):  
Full Text ◽  
Retrieval System ◽  
Text Retrieval ◽  
Full Text Retrieval

Design and Implementation of Full-Text Retrieval System for People’s Daily Annotated Corpus

2011 ◽  
Vol 135-136 ◽  
pp. 369-374
Author(s):  
Yang Sen Zhang ◽  
Gai Juan Huang
Keyword(s):  
Full Text ◽  
Retrieval System ◽  
Search Algorithm ◽  
Text Retrieval ◽  
Index Structure ◽  
Text Search ◽  
Full Text Search ◽  
People’S Daily ◽  
Full Text Retrieval ◽  
People's Daily

In this paper, we have designed and realized a efficient full-text retrieval system for the basic annotation People's Daily Corpus based on the inverted index technology. According to the characteristics of the basic annotation People’s Daily Corpus data, we have analyzed the methods and strategies of system implementing thoroughly. On the basis of comparing the various schemes, we have put forward to the three levels index structure of Chinese character, word and address set, and given the design approach of each level index dictionary structure. After converting the unstructured People’s Daily corpus into index structured data, we realized the full-text search algorithm correspond to the proposed index structure. Experimental results show that the proposed search algorithm has achieved the target of "ten millions Chinese characters, response in a second", improved the speed of the People's Daily Corpus full-text search.

A Full-Text Retrieval System with a Dynamic Abstract Generation Function

SIGIR ’94 ◽  
1994 ◽  
pp. 152-161 ◽  
Author(s):  
Seiji Miike ◽  
Etsuo Itoh ◽  
Kenji Ono ◽  
Kazuo Sumita
Keyword(s):  
Full Text ◽  
Retrieval System ◽  
Text Retrieval ◽  
Generation Function ◽  
Full Text Retrieval
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