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» Learning and Generalization with the Information Bottleneck
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ICML
2004
IEEE
15 years 10 months ago
Unifying collaborative and content-based filtering
Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user preference prediction. This paper proposes a nove...
Justin Basilico, Thomas Hofmann
CIKM
2009
Springer
15 years 4 months ago
Learning to rank from Bayesian decision inference
Ranking is a key problem in many information retrieval (IR) applications, such as document retrieval and collaborative filtering. In this paper, we address the issue of learning ...
Jen-Wei Kuo, Pu-Jen Cheng, Hsin-Min Wang
SIGIR
2011
ACM
14 years 21 days ago
Collaborative competitive filtering: learning recommender using context of user choice
While a user’s preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learni...
Shuang-Hong Yang, Bo Long, Alexander J. Smola, Hon...
EKAW
2004
Springer
15 years 3 months ago
Semantic Webs for Learning: A Vision and Its Realization
Abstract. Augmenting web pages with semantic contents, i.e., building a ‘Semantic Web’, promises a number of benefits for web users in general and learners in particular. Seman...
Arthur Stutt, Enrico Motta
AAAI
1998
14 years 11 months ago
Learning to Extract Symbolic Knowledge from the World Wide Web
The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a...
Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew M...