Differentiating Regularization Weights -- A Simple Mechanism to Alleviate Cold Start in Recommender Systems

@article{Chen2019DifferentiatingRW,
  title={Differentiating Regularization Weights -- A Simple Mechanism to Alleviate Cold Start in Recommender Systems},
  author={Hung-Hsuan Chen and Pu Chen},
  journal={ACM Transactions on Knowledge Discovery from Data (TKDD)},
  year={2019},
  volume={13},
  pages={1 - 22}
}
  • Hung-Hsuan ChenPu Chen
  • Published 9 January 2019
  • Computer Science
  • ACM Transactions on Knowledge Discovery from Data (TKDD)
Matrix factorization (MF) and its extended methodologies have been studied extensively in the community of recommender systems in the last decade. Essentially, MF attempts to search for low-ranked matrices that can (1) best approximate the known rating scores, and (2) maintain low Frobenius norm for the low-ranked matrices to prevent overfitting. Since the two objectives conflict with each other, the common practice is to assign the relative importance weights as the hyper-parameters to these… 

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