Proposed a Related PageRank Algorithm Based on the User's Interest

2013 ◽  
Vol 718-720 ◽  
pp. 2040-2044 ◽  
Author(s):  
Xiao Ping Xian ◽  
Yue Guang Li
Keyword(s):  
Search Engine ◽  
Simulation Experiment ◽  
Vector Space Model ◽  
Experimental Result ◽  
Web Pages ◽  
Pagerank Algorithm ◽  
Original Algorithm ◽  
Space Model ◽  
Better Than ◽  
Improved Algorithm

In order to improve search engine retrieval quality, using the vector space model is proposed based on the user's interest page pre-classifying and content related PageRank algorithm, the two aspects of the weights to influence of web pages with the PR value. Through the simulation experiment after improved algorithm experimental result, compared with the original algorithm, the improved algorithm precision better than the original algorithm.

scholarly journals SISTEM TEMU KEMBALI INFORMASI PADA DOKUMEN DENGAN METODE VECTOR SPACE MODEL

2017 ◽  
Vol 9 (1) ◽  
pp. 74
Author(s):  
Irmawati Irmawati
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Search Engine ◽  
Vector Space Model ◽  
Space Model

Informasi saat ini sangat mudah didapatkan dengan memanfaatkan fasilitas internet dimanapun dan kapanpun. Di sisi lain informasi yang didapat dari  search engine merupakan semua hal yang berkaitan dengan kata kunci yang dicari. Hal ini menyebabkan pengguna terpaksa menyaring untuk mendapatkan dokumen yang relevan. Oleh karena itu diperlukan cara untuk mengelompokkan banyaknya informasi yang tersedia, yang dibutuhkan pengguna sehingga memudahkan pengguna untuk mendapatkan dokumen yang diinginkan. Pada penelitian ini diusulkan suatu solusi dari permasalahan tersebut dengan mengembangkan metode ilmu pencarian yang dikenal dengan temu-kembali informasi (information retrieval) dan metode Vector Space Model (VSM). Pada metode Vector Space Model (VSM) beberapa dokumen online akan diindeks dan diurutkan berdasarkan bobot dari kata pencarian yang terdapat di dalam dokumen online tersebut. Salah satu algoritma pembobotannya adalah algoritma tf-idf yang dipengaruhi oleh frekuensi kemunculan kata pada tiap dokumen online dan frekuensi dari dokumen online yang memiliki kata tersebut.

scholarly journals Visualizing text similarities from a graph-based SOM

2015 ◽  
Vol 14 (7) ◽  
pp. 5877-5886
Author(s):  
Khalid Kahloot ◽  
Mohammad A. Mikki ◽  
Akram A. ElKhatib
Keyword(s):  
Clustering Algorithm ◽  
Vector Space Model ◽  
Graph Model ◽  
Self Organizing Map ◽  
Text Data ◽  
Pagerank Algorithm ◽  
Space Model ◽  
As Graph ◽  
Som Clustering ◽  
Text Searching

Text in articles is based on expert opinion of a large number of people including the views of authors. These views are based on cultural or community aspects, which make extracting information from text very difficult. This paper introduced how to utilize the capabilities of a modified graph-based Self-Organizing Map (SOM) in showing text similarities. Text similarities are extracted from an article using Google's PageRank algorithm. Sentences from an input article are represented as graph model instead of vector space model. The resulted graph can be shown in a visual animation for eight famous graph algorithms execution with animation speed control.The resulted graph is used as an input to SOM. SOM clustering algorithm is used to construct knowledge from text data. We used a visual animation for eight famous graph methods with animation speed control and according to similarity measure; an adjustable number of most similar sentences are arranged in visual form. In addition, this paper presents a wide variety of text searching. We had compared our project with famous clustering and visualization project in term of purity, entropy and F measure. Our project showed accepted results and mostly superiority over other projects.

An Improved Algorithm of Improved Computation Efficiency on LTS Hausdorff Distance

2013 ◽  
Vol 347-350 ◽  
pp. 3217-3221
Author(s):  
Hui Wang ◽  
Guo Jia Li ◽  
Jun Hui Pan ◽  
Fu Hua Shang
Keyword(s):  
Hausdorff Distance ◽  
Curve Segment ◽  
Original Algorithm ◽  
Search Path ◽  
Computation Efficiency ◽  
Traditional Algorithm ◽  
Better Than ◽  
Improved Algorithm

The computation efficiency of traditional algorithm is not high, and there is more time consuming. This paper presents an effective method for improved hausdorff distance, depth correction of logging curves is based on improved Hausdorff distance. In this method. On the basis of existing LTS hausdorff distance, the contrast curve segment is divided into neighborhood in an area, the LTS hausdorff distance is calculated by using engineering approximate, and the improving methods of search path is put forward, which ensures that the improved algorithm is better than the original algorithm has high computing efficiency and accuracy in theory.

Research and Realization of a Search Engine System for Professional Field

2013 ◽  
Vol 850-851 ◽  
pp. 745-750
Author(s):  
Ying Hong ◽  
Zeng Min Geng
Keyword(s):  
Search Engine ◽  
Experimental Results ◽  
Web Pages ◽  
Pagerank Algorithm ◽  
General Search ◽  
Professional Field ◽  
Engine System

In the light of the deficiency of general search engine technology in professional retrieval,This paper researched and designed a search engine system for professional field (SESPF for short).This system automatically crawls web pages by the spider program.It introduced professional dictionary and filtered the webpages information according to certain rules.At the same time,the system improved the PageRank algorithm and Lucene webpage ranking algorithm.The experimental results show that this system has a higher precision in professional field retrieval compared with the general search engine.

A Query Expansion Method Based on User's Historical Interested Web Pages and Historical Query Terms

2011 ◽  
Vol 52-54 ◽  
pp. 1218-1225
Author(s):  
Zheng Yu Zhu ◽  
Chun Lei Yu ◽  
Shu Jia Dong ◽  
Jie He
Keyword(s):  
Search Engine ◽  
Query Expansion ◽  
Expansion Method ◽  
Experimental Results ◽  
Web Pages ◽  
Average Precision ◽  
Individual User ◽  
Search Results ◽  
Current Algorithm ◽  
Better Than

Current popular search engines are built to serve all users, independent of the needs of any individual user. A personalized query expansion method based on user's historical interested Web pages (UHIWPs) and user’s historical query terms (UHQTs) is proposed in this paper. When a user submits a query keyword to a search engine, the new algorithm can automatically locate the current user’s implicit search intention and compute the term-term associations dynamically according to the user’s UHIWPs and UHQTs. More personalized expansion terms then will be generated and submitted to the search engine together with the query keyword. As a result, different search results can be returned to different users even though they input the same query keywords. Experimental results show that this method is better than the current algorithm in average precision.

An Improved Method of Judging the Theme Relativity Based on Vector Space Model in Vertical Search Engine

2013 ◽  
Vol 411-414 ◽  
pp. 106-109 ◽  
Author(s):  
Ya Heng Ren
Keyword(s):  
Vector Space ◽  
Search Engine ◽  
Vector Space Model ◽  
Improved Method ◽  
New Model ◽  
Space Model ◽  
Vertical Search ◽  
Vertical Search Engine ◽  
The Web

Vertical Search Engine provides a professional search compared with the traditional search engine. All of the data searched by vertical search engine is relative with some one theme, which is decided by users. Usually Vector Space Model is used for judging the relativity between data in the web and the decided theme. But when elements of the theme appear repeatedly, their order is not considered by Vector Space Model. Adding a new element, the Evolved Vector Space Model is provided. The experiments show that the new model has fixed the problem and have a better performance in judging relativity.

Design of a Novel Search Engine for Prospective Question Answering

2014 ◽  
Vol 4 (2) ◽  
pp. 19-40
Author(s):  
Rosy Madaan ◽  
A.K. Sharma ◽  
Ashutosh Dixit
Keyword(s):  
Information Processing ◽  
Search Engine ◽  
Question Answering ◽  
Experimental Results ◽  
Web Pages ◽  
Source Of Information ◽  
Answering Questions ◽  
Better Than

Question answering offers a more intuitive approach to information processing. A number of approaches have been used for answering questions. In this paper, we propose a questionansweringsystem that uses blogs as its source of information. The system deals with crawling blog pages, summarizing them, indexing and then ranking the summarized content. The user asks a question and gets answer(s) in response. The answer(s) obtained are better as compared to those provided by the existing QA systems that use the general web pages for the purpose of answering. The experimental results show that the proposed system has shown promising results and the responses given by the system are better than those given by the existing QA systems.

scholarly journals SISTEM TEMU KEMBALI INFORMASI PADA DOKUMEN DENGAN METODE VECTOR SPACE MODEL

2017 ◽  
Vol 9 (1) ◽  
pp. 74
Author(s):  
Irmawati Irmawati
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Search Engine ◽  
Vector Space Model ◽  
Space Model

Informasi saat ini sangat mudah didapatkan dengan memanfaatkan fasilitas internet dimanapun dan kapanpun. Di sisi lain informasi yang didapat dari  search engine merupakan semua hal yang berkaitan dengan kata kunci yang dicari. Hal ini menyebabkan pengguna terpaksa menyaring untuk mendapatkan dokumen yang relevan. Oleh karena itu diperlukan cara untuk mengelompokkan banyaknya informasi yang tersedia, yang dibutuhkan pengguna sehingga memudahkan pengguna untuk mendapatkan dokumen yang diinginkan. Pada penelitian ini diusulkan suatu solusi dari permasalahan tersebut dengan mengembangkan metode ilmu pencarian yang dikenal dengan temu-kembali informasi (information retrieval) dan metode Vector Space Model (VSM). Pada metode Vector Space Model (VSM) beberapa dokumen online akan diindeks dan diurutkan berdasarkan bobot dari kata pencarian yang terdapat di dalam dokumen online tersebut. Salah satu algoritma pembobotannya adalah algoritma tf-idf yang dipengaruhi oleh frekuensi kemunculan kata pada tiap dokumen online dan frekuensi dari dokumen online yang memiliki kata tersebut.

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