Nonparametric Preference Completion
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Proceedings of the TwentyFirst International Conference on Artificial Intelligence and Statistics, PMLR 84:632641, 2018.
Abstract
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 x_i and y_u, and that the associated rating is given by $g_u(f(x_i,y_u))$ where f is Lipschitz and g_u is a monotonic transformation that depends on the user. We propose a knearest neighborslike algorithm and prove that it is consistent. To the best of our knowledge, this is the first consistency result for the collaborative preference completion problem in a nonparametric setting. Finally, we demonstrate the performance of our algorithm with experiments on the Netflix and Movielens datasets.
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