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    KDD
    2010
    ACM
    289views Data Mining» more  KDD 2010»
    Exploitation and exploration in a performance based contextual advertising system
    13 years 7 months ago
    Exploitation and exploration in a performance based contextual advertising system
    Download  www.cs.umass.edu
    The dynamic marketplace in online advertising calls for ranking systems that are optimized to consistently promote and capitalize better performing ads. The streaming nature of on...
    Wei Li 0010, Xuerui Wang, Ruofei Zhang, Ying Cui, ...
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    ICDM
    2009
    IEEE
    152views Data Mining» more  ICDM 2009»
    A Sparsification Approach for Temporal Graphical Model Decomposition
    13 years 7 months ago
    A Sparsification Approach for Temporal Graphical Model Decomposition
    Download  www.cs.kent.edu
    Temporal causal modeling can be used to recover the causal structure among a group of relevant time series variables. Several methods have been developed to explicitly construct te...
    Ning Ruan, Ruoming Jin, Victor E. Lee, Kun Huang
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    WSDM
    2012
    ACM
    290views Data Mining» more  WSDM 2012»
    Online selection of diverse results
    12 years 5 months ago
    Online selection of diverse results
    Download  people.csail.mit.edu
    The phenomenal growth in the volume of easily accessible information via various web-based services has made it essential for service providers to provide users with personalized ...
    Debmalya Panigrahi, Atish Das Sarma, Gagan Aggarwa...
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    KDD
    2012
    ACM
    205views Data Mining» more  KDD 2012»
    Rank-loss support instance machines for MIML instance annotation
    12 years 2 days ago
    Rank-loss support instance machines for MIML instance annotation
    Download  web.engr.oregonstate.edu
    Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels....
    Forrest Briggs, Xiaoli Z. Fern, Raviv Raich
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    SDM
    2012
    SIAM
    252views Data Mining» more  SDM 2012»
    Learning from Heterogeneous Sources via Gradient Boosting Consensus
    12 years 1 days 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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