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Abstract Details
Activity Number:
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491
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Type:
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Invited
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #300122 |
Title:
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Time-Sensitive Collaborative Filtering via Item-Item Similarities
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Author(s):
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Rahul Mazumder*+ and Deepak Agarwal and Liang Zhang
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Companies:
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Stanford University and Yahoo Research and Yahoo Research
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Address:
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Department of Statistics, Sequoia Hall, Stanford, CA, 94305,
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Keywords:
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collaborative filtering ;
covariance regularization ;
sparsity (L1 regularization) ;
large scale optimization ;
expectation maximization ;
web / internet applications
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Abstract:
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Consider a generic collaborative system problem with users rating (clicking) items over time --- with potentially a large fraction of the user-item matrix unobserved. We will address how to come up with predictions/ recommendations for the unobserved entries in the matrix by incorporating item-item similarity in a model-based fashion. This novel proposal takes us well beyond the typical low-rank matrix factorization models used in collaborative filtering. Various notions of item-item similarities can be used via a prior regularization eg the sparse inverse covariance regularization accounting for interpretable conditional independencies among the items resulting in a sparse item-item similarity graph and sophisticated variants thereof. We propose computationally tractable, theoretically justified, novel algorithms that enable us to learn these complex models for large-scale recommender systems, with potentially billions of variables. We will also discuss how adopt our framework to account for dynamic user /item visit patterns and time sensitive user-item interactions, as arsing in Yahoo! front page and News applications.
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