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    SDM
    2012
    SIAM
    304views Data Mining» more  SDM 2012»
    Robust Reputation-Based Ranking on Bipartite Rating Networks
    12 years 12 hour ago
    Robust Reputation-Based Ranking on Bipartite Rating Networks
    Download  www.se.cuhk.edu.hk
    With the growth of the Internet and E-commerce, bipartite rating networks are ubiquitous. In such bipartite rating networks, there exist two types of entities: the users and the o...
    Rong-Hua Li, Jeffrey Xu Yu, Xin Huang, Hong Cheng
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    SDM
    2012
    SIAM
    216views Data Mining» more  SDM 2012»
    Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
    12 years 12 hour ago
    Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
    Download  www.hpl.hp.com
    :  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
    George Forman
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    SDM
    2012
    SIAM
    245views Data Mining» more  SDM 2012»
    Deterministic CUR for Improved Large-Scale Data Analysis: An Empirical Study
    12 years 12 hour ago
    Deterministic CUR for Improved Large-Scale Data Analysis: An Empirical Study
    Download  www-kd.iai.uni-bonn.de
    Low-rank approximations which are computed from selected rows and columns of a given data matrix have attracted considerable attention lately. They have been proposed as an altern...
    Christian Thurau, Kristian Kersting, Christian Bau...
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    SDM
    2012
    SIAM
    281views Data Mining» more  SDM 2012»
    Contextual Collaborative Filtering via Hierarchical Matrix Factorization
    12 years 13 hour ago
    Contextual Collaborative Filtering via Hierarchical Matrix Factorization
    Download  www.cse.ust.hk
    Matrix factorization (MF) has been demonstrated to be one of the most competitive techniques for collaborative filtering. However, state-of-the-art MFs do not consider contextual...
    ErHeng Zhong, Wei Fan, Qiang Yang
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    SDM
    2012
    SIAM
    252views Data Mining» more  SDM 2012»
    Learning from Heterogeneous Sources via Gradient Boosting Consensus
    12 years 12 hour ago
    Learning from Heterogeneous Sources via Gradient Boosting Consensus
    Download  david.grangier.info
    Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
    Xiaoxiao Shi, Jean-François Paiement, David...
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