Earticle

초록

영어

Time is an important dimension of information space. It plays important roles in Web search, because most Web pages contain time information and many Web queries are time-related. Therefore, exploiting temporal information in Web pages has been a hotspot in the research on Web search. In this paper, we focus on the time-enhanced topic clustering issue for news search results. Traditional clustering algorithms are usually based on the common phrases of Web pages, and they have little consideration about using the temporal information of Web pages. From this perspective, we propose a time-enhanced topic clustering algorithm for news Web pages. It improves traditional algorithms which only consider textual clustering, and applies a temporal clustering procedure on the topics returned by a textual clustering algorithm, which is to arrange every Web page in a cluster along a timeline based on the update time in Web pages. We conduct experiments on a real dataset crawled from Google News, and compare our algorithm with other competitors including K-Means, STC, TFIC, and Minhash Clustering in terms of different metrics such as precision and recall. The experimental results show that the proposed algorithm has better performance under both offline and online clustering test.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Time-Enhanced Topic Clustering
  3.1 Offline Clustering
  3.2 Online Clustering
  3.3 Time-Based Clustering
 4. Performance Evaluation
  4.1 Dataset
  4.2 Results
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Jie Zhao [ School of Business, Anhui University, School of Management, University of Science and Technology of China ]
• Xiaowen Li [ School of Computer Science and Technology, University of Science and Technology of China ]
• Peiquan Jin [ School of Computer Science and Technology, University of Science and Technology of China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605