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» Modeling and predicting user behavior in sponsored search
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AAAI
2004
13 years 9 months ago
Learning and Inferring Transportation Routines
This paper introduces a hierarchical Markov model that can learn and infer a user's daily movements through the commue model uses multiple levels of abstraction in order to b...
Lin Liao, Dieter Fox, Henry A. Kautz
SIGIR
2008
ACM
13 years 7 months ago
EigenRank: a ranking-oriented approach to collaborative filtering
A recommender system must be able to suggest items that are likely to be preferred by the user. In most systems, the degree of preference is represented by a rating score. Given a...
Nathan Nan Liu, Qiang Yang
EMSOFT
2005
Springer
14 years 1 months ago
AutoDVS: an automatic, general-purpose, dynamic clock scheduling system for hand-held devices
We present AutoDVS, a dynamic voltage scaling (DVS) system for hand-held computers. Unlike extant DVS systems, AutoDVS distinguishes common, course-grain, program behavior and cou...
Selim Gurun, Chandra Krintz
BMCBI
2008
208views more  BMCBI 2008»
13 years 7 months ago
GraphFind: enhancing graph searching by low support data mining techniques
Background: Biomedical and chemical databases are large and rapidly growing in size. Graphs naturally model such kinds of data. To fully exploit the wealth of information in these...
Alfredo Ferro, Rosalba Giugno, Misael Mongiov&igra...
KDD
2012
ACM
187views Data Mining» more  KDD 2012»
11 years 10 months ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...