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VLDB
2008
ACM
170views Database» more  VLDB 2008»
14 years 5 months ago
A multi-ranker model for adaptive XML searching
The evolution of computing technology suggests that it has become more feasible to offer access to Web information in a ubiquitous way, through various kinds of interaction device...
Ho Lam Lau, Wilfred Ng
SIGIR
2012
ACM
11 years 7 months ago
Top-k learning to rank: labeling, ranking and evaluation
In this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes from the difficul...
Shuzi Niu, Jiafeng Guo, Yanyan Lan, Xueqi Cheng
EMNLP
2009
13 years 2 months ago
Model Adaptation via Model Interpolation and Boosting for Web Search Ranking
This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm. The res...
Jianfeng Gao, Qiang Wu, Chris Burges, Krysta Marie...
MIR
2006
ACM
200views Multimedia» more  MIR 2006»
13 years 11 months ago
An adaptive graph model for automatic image annotation
Automatic keyword annotation is a promising solution to enable more effective image search by using keywords. In this paper, we propose a novel automatic image annotation method b...
Jing Liu, Mingjing Li, Wei-Ying Ma, Qingshan Liu, ...
WWW
2006
ACM
14 years 5 months ago
Beyond PageRank: machine learning for static ranking
Since the publication of Brin and Page's paper on PageRank, many in the Web community have depended on PageRank for the static (query-independent) ordering of Web pages. We s...
Matthew Richardson, Amit Prakash, Eric Brill