Nonparametric Preference Completion

@inproceedings{KatzSamuels2018NonparametricPC,
  title={Nonparametric Preference Completion},
  author={Julian Katz-Samuels and Clayton Scott},
  booktitle={AISTATS},
  year={2018}
}
We consider the task of collaborative preference completion: given a pool of items, a pool of users and a partially observed itemuser rating matrix, the goal is to recover the personalized ranking of each user over all of the items. Our approach is nonparametric: we assume that each item i and each user u have unobserved features xi and yu, and that the associated rating is given by gupfpxi, yuqq where f is Lipschitz and gu is a monotonic transformation that depends on the user. We propose a k… CONTINUE READING
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