资源分类
》 资源 》论文》Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm

Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm

2020-01-06 | |  63 |   36 |   0

Abstract

We show that matrix completion with trace-norm regularization can be significantly hurt when entries of the matrix are sampled non-uniformly, but that a properly weighted version of the trace-norm regularizer works well with non-uniform sampling. We show that the weighted trace-norm regularization indeed yields significant gains on the highly non-uniformly sampled Netflix dataset.

上一篇：Decoding Ipsilateral Finger Movements from ECoG Signals in Humans

下一篇：Learning via Gaussian Herding

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...