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 P. Chen},
  journal={ACM Transactions on Knowledge Discovery from Data (TKDD)},
  year={2019},
  volume={13},
  pages={1 - 22}
}
  • Hung-Hsuan Chen, P. Chen
  • Published 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… CONTINUE READING
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