On the Regularization Landscape for the Linear Recommendation Models
Study shows leading linear recommendation models reduce to nuclear-norm or Frobenius-norm regularization, with two new closed-form low-rank solutions proposed.
The paper unifies top-performing linear recommendation algorithms under a single regularization framework, showing they effectively apply either nuclear-norm or Frobenius-norm regularizers. Nuclear-norm solutions have a rigid structure, are low-rank, and have closed form, while Frobenius-norm solutions are more expressive but full-rank or require hard-to-tune procedures such as ADMM. The authors derive two new low-rank, closed-form solutions that combine the advantages of both regularization families.
- Leading linear recommenders share nuclear- or Frobenius-norm regularization
- Nuclear-norm models are rigid but yield closed-form low-rank solutions
- Frobenius-norm models are expressive but full-rank or ADMM-dependent
- Two new low-rank closed-form solutions combine both advantages
From the original article
1 min read at arxiv.orgRecently, a wide range of recommendation algorithms inspired by deep learning techniques have emerged as the performance leaders on several standard recommendation benchmarks. While these algorithms were built on different DL techniques (e. g.
By Dong Li · arxiv.org
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