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Activity Number:
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445
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #320328
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Title:
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Clustering for Personalized Preference Prediction
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Author(s):
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Fan Yang* and Xiaotong Shen
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Companies:
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University of Minnesota - Twin Cities and University of Minnesota
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Keywords:
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Clustering ;
Personalized ;
Recommendation
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Abstract:
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We build a model to allow personalized prediction for different individuals on a large amount of items based on both user features and item features, as in a recommender system. User and item "preferences" are clustered through supervised learning by modeling the observed response with a gaussian distributed regression model. Besides mean parameters, correlation structure of the response variable is also modeled. Fusion type penalties are applied to identify similar users and items. Simulation results show our model performs better than the popular matrix decomposition methods.
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Authors who are presenting talks have a * after their name.
