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» Learning to rank for information retrieval (LR4IR 2008)
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TREC
2008
13 years 10 months ago
A Hybrid Method for Opinion finding Task (KUNLP at TREC 2008 Blog Track)
This paper presents an approach for the Opinion Finding task at TREC 2008 Blog Track. For the Ad-hoc Retrieval subtask, we adopt language model to retrieve relevant documents. For...
Linh Hoang, Seung-Wook Lee, Gum-Won Hong, Joo-Youn...
TREC
2008
13 years 10 months ago
FEUP at TREC 2008 Blog Track: Using Temporal Evidence for Ranking and Feed Distillation
This paper presents the participation of FEUP, from University of Porto, in the TREC 2008 Blog Track. FEUP participated in two tasks, the baseline adhoc retrieval task and the blo...
Sérgio Nunes, Cristina Ribeiro, Gabriel Dav...
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...
CIKM
2009
Springer
14 years 3 months ago
Learning to rank from Bayesian decision inference
Ranking is a key problem in many information retrieval (IR) applications, such as document retrieval and collaborative filtering. In this paper, we address the issue of learning ...
Jen-Wei Kuo, Pu-Jen Cheng, Hsin-Min Wang
SIGIR
2008
ACM
13 years 8 months ago
Learning to rank with partially-labeled data
Ranking algorithms, whose goal is to appropriately order a set of objects/documents, are an important component of information retrieval systems. Previous work on ranking algorith...
Kevin Duh, Katrin Kirchhoff