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» A New Email Retrieval Ranking Approach
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ECIR
2009
Springer
14 years 3 months ago
Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve
Abstract. Learning ranking functions is crucial for solving many problems, ranging from document retrieval to building recommendation systems based on an individual user’s prefer...
Pinar Donmez, Jaime G. Carbonell
DGO
2008
128views Education» more  DGO 2008»
13 years 7 months ago
Ontology generation for large email collections
This paper presents a new approach to identifying concepts expressed in a collection of email messages, and organizing them into an ontology or taxonomy for browsing. It incorpora...
Hui Yang, Jamie Callan
EDBT
2006
ACM
169views Database» more  EDBT 2006»
14 years 6 months ago
Feedback-Driven Structural Query Expansion for Ranked Retrieval of XML Data
Relevance Feedback is an important way to enhance retrieval quality by integrating relevance information provided by a user. In XML retrieval, feedback engines usually generate an ...
Ralf Schenkel, Martin Theobald
CIKM
2005
Springer
13 years 8 months ago
Using RankBoost to compare retrieval systems
This paper presents a new pooling method for constructing the assessment sets used in the evaluation of retrieval systems. Our proposal is based on RankBoost, a machine learning v...
Huyen-Trang Vu, Patrick Gallinari
ICML
2007
IEEE
14 years 7 months ago
Learning to rank: from pairwise approach to listwise approach
The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative fil...
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Han...