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» Term Ranking for Clustering Web Search Results
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KDD
2004
ACM
145views Data Mining» more  KDD 2004»
14 years 2 months ago
A graph-theoretic approach to extract storylines from search results
We present a graph-theoretic approach to discover storylines from search results. Storylines are windows that offer glimpses into interesting themes latent among the top search re...
Ravi Kumar, Uma Mahadevan, D. Sivakumar
WWW
2006
ACM
14 years 9 months ago
CWS: a comparative web search system
In this paper, we define and study a novel search problem: Comparative Web Search (CWS). The task of CWS is to seek relevant and comparative information from the Web to help users...
Jian-Tao Sun, Xuanhui Wang, Dou Shen, Hua-Jun Zeng...
WWW
2004
ACM
14 years 9 months ago
Affinity rank: a new scheme for efficient web search
Maximizing only the relevance between queries and documents will not satisfy users if they want the top search results to present a wide coverage of topics by a few representative...
Yi Liu, Benyu Zhang, Zheng Chen, Michael R. Lyu, W...
DKE
2006
118views more  DKE 2006»
13 years 8 months ago
Clustering e-commerce search engines based on their search interface pages using WISE-Cluster
In this paper, we propose a new approach to clustering e-commerce search engines (ESEs) on the Web. Our approach utilizes the features available on the interface page of each ESE,...
Yiyao Lu, Hai He, Qian Peng, Weiyi Meng, Clement T...
WWW
2008
ACM
14 years 9 months ago
Learning to rank relational objects and its application to web search
Learning to rank is a new statistical learning technology on creating a ranking model for sorting objects. The technology has been successfully applied to web search, and is becom...
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang...