Nonparametric Preference Completion

  title={Nonparametric Preference Completion},
  author={Julian Katz-Samuels and Clayton Scott},
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
Related Discussions
This paper has been referenced on Twitter 9 times. VIEW TWEETS

From This Paper

Figures, tables, and topics from this paper.

Explore Further: Topics Discussed in This Paper


Publications citing this paper.
Showing 1-2 of 2 extracted citations


Publications referenced by this paper.
Showing 1-10 of 29 references

A Comprehensive Survey of Neighborhood-based Recommendation Methods

Recommender Systems Handbook • 2011
View 6 Excerpts
Highly Influenced

Individualized rank aggregation using nuclear norm regularization

2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton) • 2015
View 2 Excerpts