University of Illinois Urbana-Champaign

Computational Community Interest and Comments Centric Analysis Ranking

Liu, Xiaozhong; Brzeski, Vadim

Content Files
Computational_community_interest_and_comments_centric_analysis_ranking-Poster-21.pdf

Permalink

https://hdl.handle.net/2142/15272

Description

Title
Computational Community Interest and Comments Centric Analysis Ranking
Author(s)
  • Liu, Xiaozhong
  • Brzeski, Vadim
Issue Date
2009-02-08
Keyword(s)
  • Information Retrieval
  • Ranking
  • Topic
  • Comment
  • User
  • Blog
  • Community Interest
  • LDA
Date of Ingest
2010-03-30T16:00:55Z
Abstract
Ranking is an important subject in information retrieval, and a variety of techniques and algorithms have been developed to rank the retrieved documents and web pages for a given query. However, ranking is also a challenging task, since it is a dynamic problem, namely a user’s interest toward each query changes from time to time and it is difficult to accurately extract user interest over time. In this paper, we propose an innovative method to extract and weight real time community interested topic for ranking. By generating community interest vector (CIV), we compute the probability score that community interests in specific document or web page in the search results based on daily or past few hours user-oriented data, and use this score for ranking.
Type of Resource
text
Genre of Resource
Conference Poster
Language
en
Permalink
http://hdl.handle.net/2142/15272

Owning Collections

iConference 2009 Posters PRIMARY
Posters presented at the iConference 2009