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» Learning to rank for information retrieval (LR4IR 2008)
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CIVR
2007
Springer
16 years 10 days ago
Semantics reinforcement and fusion learning for multimedia streams
Fusion of multimedia streams for enhanced performance is a critical problem for retrieval. However, fusion performance tends to easily overfit the hillclimb set used to learn fus...
Dhiraj Joshi, Milind R. Naphade, Apostol Natsev
AIRWEB
2009
Springer
16 years 23 days ago
Looking into the past to better classify web spam
Web spamming techniques aim to achieve undeserved rankings in search results. Research has been widely conducted on identifying such spam and neutralizing its influence. However,...
Na Dai, Brian D. Davison, Xiaoguang Qi
ECIR
2008
Springer
15 years 7 months ago
Utilizing Passage-Based Language Models for Document Retrieval
Abstract. We show that several previously proposed passage-based document ranking principles, along with some new ones, can be derived from the same probabilistic model. We use lan...
Michael Bendersky, Oren Kurland
WWW
2011
ACM
15 years 1 months ago
Learning to re-rank: query-dependent image re-ranking using click data
Our objective is to improve the performance of keyword based image search engines by re-ranking their baseline results. To this end, we address three limitations of existing searc...
Vidit Jain, Manik Varma
SIGIR
2012
ACM
13 years 8 months ago
TFMAP: optimizing MAP for top-n context-aware recommendation
In this paper, we tackle the problem of top-N context-aware recommendation for implicit feedback scenarios. We frame this challenge as a ranking problem in collaborative filterin...
Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, ...