Blog Search Method using User Relevance Feedback and Guru Estimation 


Vol. 15,  No. 5, pp. 487-492, Oct.  2008
10.3745/KIPSTB.2008.15.5.487


PDF
  Abstract

Most Web search engines use ranking methods that take both the relevancy and the importance of documents into consideration. The importance of a document denotes the degree of usefulness of the document to general users. One of the most successful methods for estimating the importance of a document has been Page-Rank algorithm which uses the hyperlink structure of the Web for the estimation. In this paper, we propose a new importance estimation algorithm for the blog environment. The proposed method, first, calculates the importance of each document using user's bookmark and click count. Then, the Guru point of a blogger is computed as the sum of all importance points of documents which he/she wrote. Finally, the guru points are reflected in document ranking again. Our experiments show that the proposed method has higher correlation coefficient than the traditional methods with respect to correct answers.

  Statistics
  Cite this article

[IEEE Style]

K. S. Jeong and H. R. Park, "Blog Search Method using User Relevance Feedback and Guru Estimation," The KIPS Transactions:PartB , vol. 15, no. 5, pp. 487-492, 2008. DOI: 10.3745/KIPSTB.2008.15.5.487.

[ACM Style]

Kyung Seok Jeong and Hyuk Ro Park. 2008. Blog Search Method using User Relevance Feedback and Guru Estimation. The KIPS Transactions:PartB , 15, 5, (2008), 487-492. DOI: 10.3745/KIPSTB.2008.15.5.487.

Forms

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)

Old Journals

Indexing

All publications of TKIPS is indexed in DOI, EBSCO, Google Scholar, Crossref, and CrossCheck.

TKIPS is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).

Related Journals