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IJAR
2006
89views more  IJAR 2006»
14 years 9 months ago
Learning probabilistic decision graphs
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
Manfred Jaeger, Jens D. Nielsen, Tomi Silander
DEXA
2010
Springer
180views Database» more  DEXA 2010»
14 years 11 months ago
Identifying Interesting Instances for Probabilistic Skylines
Significant research efforts have recently been dedicated to modeling and querying uncertain data. In this paper, we focus on skyline analysis of uncertain data, modeled as uncert...
Yinian Qi, Mikhail J. Atallah
ACMICEC
2007
ACM
168views ECommerce» more  ACMICEC 2007»
15 years 1 months ago
Designing novel review ranking systems: predicting the usefulness and impact of reviews
With the rapid growth of the Internet, users' ability to publish content has created active electronic communities that provide a wealth of product information. Consumers nat...
Anindya Ghose, Panagiotis G. Ipeirotis
SIGIR
2009
ACM
15 years 4 months ago
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects
Social tagging is becoming increasingly popular in many Web 2.0 applications where users can annotate resources (e.g. Web pages) with arbitrary keywords (i.e. tags). A tag recomme...
Ziyu Guan, Jiajun Bu, Qiaozhu Mei, Chun Chen, Can ...
CIKM
2010
Springer
14 years 8 months ago
Learning to rank relevant and novel documents through user feedback
We consider the problem of learning to rank relevant and novel documents so as to directly maximize a performance metric called Expected Global Utility (EGU), which has several de...
Abhimanyu Lad, Yiming Yang