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《Learning Recommender Systems with Adaptive Regularization》.pdf
Learning Recommender Systems
with Adaptive Regularization
Steffen Rendle
Social Network Analysis
University of Konstanz
78457 Konstanz, Germany
steffen.rendle@uni-konstanz.de
ABSTRACT 1. INTRODUCTION
Many factorization models like matrix or tensor factoriza- Recommender systems are an important tool with many
tion have been proposed for the important application of applications e.g. online-shopping, video rental, personalized
recommender systems. The success of such factorization web sites, etc. Recently factorization models became very
models depends largely on the choice of good values for popular due to their success in several challenges including
the regularization parameters. Without a careful selection the Netflix prize1. Most of the research in this field focuses
they result in poor prediction quality as they either under- on new and improved models, among them are matrix fac-
fit or overfit the data. Regularization values are typically torization [16, 12, 18], probabilistic latent semantic analysis
determined by an expensive search that requires learning (PLSA) [6], SVD++ [7], SoRec [11], time-variant matrix fac-
the model parameters several times: once for each tuple of torization [8] for collaborative filtering or Tucker decomposi-
candidate values for the regularization parameters. In this tion
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