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SIGIR
2012
ACM
11 years 9 months ago
Top-k learning to rank: labeling, ranking and evaluation
In this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes from the difficul...
Shuzi Niu, Jiafeng Guo, Yanyan Lan, Xueqi Cheng
CIKM
2008
Springer
13 years 8 months ago
Are click-through data adequate for learning web search rankings?
Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examp...
Zhicheng Dou, Ruihua Song, Xiaojie Yuan, Ji-Rong W...
SAC
2008
ACM
13 years 6 months ago
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods ...
Karen H. L. Tso-Sutter, Leandro Balby Marinho, Lar...
VLDB
1994
ACM
147views Database» more  VLDB 1994»
13 years 10 months ago
Integrating a Structured-Text Retrieval System with an Object-Oriented Database System
We describe the integration of a structuredtext retrieval system (TextMachine) into an object-oriented database system (OpenODB). We use the external function capability of the da...
Tak W. Yan, Jurgen Annevelink
SPIRE
2009
Springer
14 years 1 months ago
Sketching Algorithms for Approximating Rank Correlations in Collaborative Filtering Systems
Collaborative filtering (CF) shares information between users to provide each with recommendations. Previous work suggests using sketching techniques to handle massive data sets i...
Yoram Bachrach, Ralf Herbrich, Ely Porat