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STAIRS
2008
169views Education» more  STAIRS 2008»
13 years 10 months ago
Probabilistic Association Rules for Item-Based Recommender Systems
Since the beginning of the 1990's, the Internet has constantly grown, proposing more and more services and sources of information. The challenge is no longer to provide users ...
Sylvain Castagnos, Armelle Brun, Anne Boyer
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
14 years 9 months ago
Practical learning from one-sided feedback
In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class. Such applications include spam filt...
D. Sculley
ICML
2007
IEEE
14 years 10 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...
JCDL
2004
ACM
146views Education» more  JCDL 2004»
14 years 2 months ago
Enhancing digital libraries with TechLens+
The number of research papers available is growing at a staggering rate. Researchers need tools to help them find the papers they should read among all the papers published each y...
Roberto Torres, Sean M. McNee, Mara Abel, Joseph A...
WWW
2009
ACM
14 years 9 months ago
Matchbox: large scale online bayesian recommendations
We present a probabilistic model for generating personalised recommendations of items to users of a web service. The Matchbox system makes use of content information in the form o...
David H. Stern, Ralf Herbrich, Thore Graepel