Publication Type

Journal Article

Version

submittedVersion

Publication Date

9-2021

Abstract

We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to matrix completion based on a sum of multiple orthonormal side information terms, together with nuclear-norm regularization. The approach allows us to inject prior knowledge about the singular vectors of the ground-truth matrix. We optimize the approach by a provably converging algorithm, which optimizes all components of the model simultaneously. We study the generalization capabilities of our method in both the distribution-free setting and in the case where the sampling distribution admits uniform marginals, yielding learning guarantees that improve with the quality of the injected knowledge in both cases. As particular cases of our framework, we present models that can incorporate user and item biases or community information in a joint and additive fashion. We analyze the performance of OMIC on several synthetic and real datasets. On synthetic datasets with a sliding scale of user bias relevance, we show that OMIC better adapts to different regimes than other methods. On real-life datasets containing user/items recommendations and relevant side information, we find that OMIC surpasses the state of the art, with the added benefit of greater interpretability.

Keywords

Optimization, Recommender systems, Training, Social networking (online), Predictive models, Learning systems, Machine learning, recommender systems, statistical learning

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Neural Networks and Learning Systems

First Page

1

Last Page

12

ISSN

2162-2388

Identifier

10.1109/TNNLS.2021.3106155

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TNNLS.2021.3106155

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