Nonparametric Preference Completion

Julian Katz-Samuels, Clayton Scott
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:632-641, 2018.

Abstract

We consider the task of collaborative preference completion: given a pool of items, a pool of users and a partially observed item-user 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 k-nearest neighbors-like 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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v84-katz-samuels18a, title = {Nonparametric Preference Completion}, author = {Julian Katz-Samuels and Clayton Scott}, booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics}, pages = {632--641}, year = {2018}, editor = {Amos Storkey and Fernando Perez-Cruz}, volume = {84}, series = {Proceedings of Machine Learning Research}, month = {09--11 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v84/katz-samuels18a/katz-samuels18a.pdf}, url = { http://proceedings.mlr.press/v84/katz-samuels18a.html }, abstract = {We consider the task of collaborative preference completion: given a pool of items, a pool of users and a partially observed item-user 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 k-nearest neighbors-like 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. } }
Endnote
%0 Conference Paper %T Nonparametric Preference Completion %A Julian Katz-Samuels %A Clayton Scott %B Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2018 %E Amos Storkey %E Fernando Perez-Cruz %F pmlr-v84-katz-samuels18a %I PMLR %P 632--641 %U http://proceedings.mlr.press/v84/katz-samuels18a.html %V 84 %X We consider the task of collaborative preference completion: given a pool of items, a pool of users and a partially observed item-user 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 k-nearest neighbors-like 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.
APA
Katz-Samuels, J. & Scott, C.. (2018). Nonparametric Preference Completion. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 84:632-641 Available from http://proceedings.mlr.press/v84/katz-samuels18a.html .

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