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» Learning to rank for information retrieval
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MM
2005
ACM
172views Multimedia» more  MM 2005»
15 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
15 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
14 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 11 months 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
15 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