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» Learning to rank for information retrieval
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MM
2005
ACM
172views Multimedia» more  MM 2005»
14 years 3 months ago
Learning the semantics of multimedia queries and concepts from a small number of examples
In this paper we unify two supposedly distinct tasks in multimedia retrieval. One task involves answering queries with a few examples. The other involves learning models for seman...
Apostol Natsev, Milind R. Naphade, Jelena Tesic
INEX
2009
Springer
14 years 2 months ago
Combining Term-Based and Category-Based Representations for Entity Search
We describe our participation in the INEX 2009 Entity Ranking track. We employ a probabilistic retrieval model for entity search in which term-based and category-based representati...
Krisztian Balog, Marc Bron, Maarten de Rijke, Wout...
TREC
2004
13 years 11 months ago
Can We Get A Better Retrieval Function From Machine?
The quality of an information retrieval system heavily depends on its retrieval function, which returns a similarity measurement between the query and each document in the collect...
Weiguo Fan, Wensi Xi, Edward A. Fox, Li Wang
MIR
2010
ACM
163views Multimedia» more  MIR 2010»
14 years 1 days ago
SNDocRank: a social network-based video search ranking framework
Multimedia ranking algorithms are usually user-neutral and measure the importance and relevance of documents by only using the visual contents and meta-data. However, users’ int...
Liang Gou, Hung-Hsuan Chen, Jung-Hyun Kim, Xiaolon...
SIGIR
2003
ACM
14 years 3 months ago
Automatic ranking of retrieval systems in imperfect environments
The empirical investigation of the effectiveness of information retrieval (IR) systems requires a test collection, a set of query topics, and a set of relevance judgments made by ...
Rabia Nuray, Fazli Can