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» Learning to rank for information retrieval
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CIKM
2010
Springer
15 years 4 months ago
Computing the top-k maximal answers in a join of ranked lists
Complex search tasks that utilize information from several data sources, are answered by integrating the results of distinct basic search queries. In such integration, each basic ...
Mirit Shalem, Yaron Kanza
AACC
2004
Springer
15 years 11 months ago
Mining Top - k Ranked Webpages Using Simulated Annealing and Genetic Algorithms
Searching on the Internet has grown in importance over the last few years, as huge amount of information is invariably accumulated on the Web. The problem involves locating the des...
P. Deepa Shenoy, K. G. Srinivasa, Achint Oommen Th...
CVPR
2000
IEEE
16 years 8 months ago
Optimizing Learning in Image Retrieval
Combining learning with vision techniques in interactive image retrieval has been an active research topic during the past few years. However, existing learning techniques either ...
Yong Rui, Thomas S. Huang
ECIR
2008
Springer
15 years 7 months ago
Enhancing Relevance Models with Adaptive Passage Retrieval
Passage retrieval and pseudo relevance feedback/query expansion have been reported as two effective means for improving document retrieval in literature. Relevance models, while im...
Xiaoyan Li, Zhigang Zhu
CIKM
2010
Springer
15 years 4 months ago
Online learning for recency search ranking using real-time user feedback
Traditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance of documents for a given query. Although such a batch-learni...
Taesup Moon, Lihong Li, Wei Chu, Ciya Liao, Zhaohu...