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Volumn , Issue , 2004, Pages 353-360

A nonparametric hierarchical bayesian framework for information filtering

Author keywords

Collaborative Filtering; Content Based Filtering; Dirichlet Process; Nonparametric Bayesian Modelling

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; HIERARCHICAL SYSTEMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBLEM SOLVING; SET THEORY;

EID: 8644266158     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1008992.1009053     Document Type: Conference Paper
Times cited : (34)

References (21)
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    • Mooney, R.1    Roy, L.2
  • 11
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    • Markov chain sampling methods for dirichlet process mixture models
    • Dept. of Statistics, University of Toronto
    • R. M. Neal. Markov chain sampling methods for dirichlet process mixture models. Technical Report 9815, Dept. of Statistics, University of Toronto.
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  • 12
    • 0033325071 scopus 로고    scopus 로고
    • A framework for collaborative, content-based and demographic filtering
    • M. Pazzani. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6):393-408, 1999.
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    • Probabilities for SV machines
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    • Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments
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    • A. Popescul, L. Ungar, D. Pennock, and S. Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In 17th Conference on Uncertainty in Artificial Intelligence, pages 437-444, Seattle, Washington, August 2-5 2001.
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    • Infinite mixtures of gaussian process experts
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  • 20


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