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106
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FLAIRS
2006
15 years 4 months ago
Adaptive Learning in Machine Summarization
In this paper, we propose a novel framework for extractive summarization. Our framework allows the summarizer to adapt and improve itself. Experimental results show that our summa...
Zhuli Xie, Barbara Di Eugenio, Peter C. Nelson
121
Voted
WWW
2009
ACM
16 years 4 months ago
A dynamic bayesian network click model for web search ranking
As with any application of machine learning, web search ranking requires labeled data. The labels usually come in the form of relevance assessments made by editors. Click logs can...
Olivier Chapelle, Ya Zhang
106
Voted
RECSYS
2009
ACM
15 years 10 months ago
Collaborative prediction and ranking with non-random missing data
A fundamental aspect of rating-based recommender systems is the observation process, the process by which users choose the items they rate. Nearly all research on collaborative ï¬...
Benjamin M. Marlin, Richard S. Zemel
118
Voted
MKM
2007
Springer
15 years 9 months ago
Methods of Relevance Ranking and Hit-content Generation in Math Search
To be effective and useful, math search systems must not only maximize precision and recall, but also present the query hits in a form that makes it easy for the user to identify...
Abdou Youssef
140
Voted
IJAIT
2008
146views more  IJAIT 2008»
15 years 3 months ago
Learning to Behave in Space: a Qualitative Spatial Representation for Robot Navigation with Reinforcement Learning
ion mechanism to create a representation of space consisting of the circular order of detected landmarks and the relative position of walls towards the agent's moving directio...
Lutz Frommberger