Optimizing personalized retrieval system based on web ranking

Authors:
Hao-ming Wang;Ye Guo;Bo-qin Feng
Affiliations:
School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China;School of Information, Xi'an University of Finance & Economics, Xi'an, Shaanxi, P.R. China;School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China
Venue:
CSR'06 Proceedings of the First international computer science conference on Theory and Applications
Year:
2006

Citing 8
Cited 0

The stochastic approach for link-structure analysis (SALSA) and the TKC effect

Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
What is this page known for? Computing Web page reputations

Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
A comprehensive agent-based architecture for intelligent information retrieval in a distributed heterogeneous environment

Decision Support Systems
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Cross-Lingual Document Similarity Calculation Using the Multilingual Thesaurus EUROVOC

CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Weighted PageRank Algorithm

CNSR '04 Proceedings of the Second Annual Conference on Communication Networks and Services Research
Inside PageRank

ACM Transactions on Internet Technology (TOIT)
Beyond TFIDF weighting for text categorization in the vector space model

IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper drew up a personalized recommender system model combined the text categorization with the pagerank. The document or the page was considered in two sides: the content of the document and the domain it belonged to. The features were extracted in order to form the feature vector, which would be used in computing the difference between the documents or keywords with the user's interests and the given domain. It set up the structure of four block levels in information management of a website. The link information was downloaded in the domain block level, which is the top level of the structure. In the host block level, the links were divided into two parts, the inter-link and the intra-link. All links were setup with different weights. The stationary eigenvector of the link matrix was calculated. The final order of documents was determined by the vector distance and the eigenvector of the link matrix.