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Title: Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information
Incorporating graph side information into recommender systems has been widely used to better predict ratings, but relatively few works have focused on theoretical guarantees. Ahn et al. (2018) firstly characterized the optimal sample complexity in the presence of graph side information, but the results are limited due to strict, unrealistic assumptions made on the unknown latent preference matrix and the structure of user clusters. In this work, we propose a new model in which 1) the unknown latent preference matrix can have any discrete values, and 2) users can be clustered into multiple clusters, thereby relaxing the assumptions made in prior work. Under this new model, we fully characterize the optimal sample complexity and develop a computationally-efficient algorithm that matches the optimal sample complexity. Our algorithm is robust to model errors and outperforms the existing algorithms in terms of prediction performance on both synthetic and real data.  more » « less
Award ID(s):
2023239
PAR ID:
10272159
Author(s) / Creator(s):
;
Editor(s):
Meila, Marina; Zhang, Tong
Date Published:
Journal Name:
Proceedings of Machine Learning Research
Volume:
139
ISSN:
2640-3498
Page Range / eLocation ID:
5107 - 5117
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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