Earticle

초록

영어

We browse through hundreds of Deep web pages everyday to find information of interest. We feel happy when Deep web browsing operations provide us with necessary information; otherwise, we feel bitter. Now, the measurement of this user satisfaction has become a hot research topic. In this paper, we propose a click-through-data-based and unsupervised user satisfaction evaluation system, CNEITE, to evaluate the user satisfaction of Deep Web query result pages. It applies query type classifying, navigational query evaluating, informational/transactional query evaluating to solve the challenging tasks. We evaluated our CNEITE system on the AOL data sets, experimental results show that CNEITE achieves higher classify precision than a widely used classify method , Dtree, and higher annotate answer accuracy than method proposed in [17].

목차

Abstract
 1. Introduction
 2. CNEITE Framework
 3. Query Type Classification Using Click through Data
  3.1. Key-URL Similarity (KUS) Evidence
  3.2. A Learning Based Classification Algorithm
 4 Automatic Navigational Query Satisfaction Evaluation with Click through Data Analysis
 5 Automatic Informational/Transactional Query Satisfaction Evaluation with Click through Data Analysis
  5.1. Automatically Correlation Annotation
  5.2. Automatically User Satisfaction Evaluation
 6. Experiments and Discussions
  6.1. Query Type Classification Experiment
  6.2. Navigational Query Correct Answer Annotation Experiment
  6.3. User Satisfaction Evaluation Experiment
 7. Conclusions and Future Work
 Acknowledgment
 References

키워드

저자

• Zhen Liu [ College of Computer Science, Chongqing University, Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing Medicine Exchange, Chongqing 400010, China ]
• Yong Feng [ Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University ]
• Huijuan Wang [ Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605