Name
Abstract | ||
|---|---|---|
Blogs are increasingly accepted as a useful means to proliferate a variety of information on the web. As the popularity of blogs grows rapidly, a number of blog search engines have appeared recently to help users access and discover blog posts efficiently. Nevertheless, existing approaches tend to focus on ranking the blog posts according to their recency or popularity only, leaving the problem of retrieving more topic relevant posts to a user's query largely unexplored. In this paper, we present a novel blog ranking framework, called PTRank, that improves search quality by taking account of relevance feedback from users as well as various information available from RSS feeds. A neural network method is employed to learn ranking functions that provide a relevance score between a keyword and a blog post. Extensive experiments on real blog data have been conducted to validate the proposed ranking framework for blog post search, and the results indicate that PTRank performs significantly better than the existing popular approach. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s11280-009-0067-3 | World Wide Web |
Keywords | Field | DocType |
blog post search,ranking,learning to rank,RSS feed,neural network,web 2.0 | Data mining,Learning to rank,World Wide Web,Relevance feedback,Information retrieval,Ranking,Computer science,Popularity,Ranking (information retrieval),Web 2.0,RSS,Spam blog | Journal |
Volume | Issue | ISSN |
12 | 4 | 1386-145X |
Citations | PageRank | References |
10 | 0.49 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seung-Kyun Han | 1 | 19 | 2.08 |
| Dongmin Shin | 2 | 118 | 16.11 |
| Jae-Yoon Jung | 3 | 297 | 31.94 |
| Jonghun Park | 4 | 491 | 37.86 |