Factorized Structured Regression for Large-Scale Varying Coefficient Models

MCML Authors

David Rügamer

Prof. Dr.

Principal Investigator

Statistics, Data Science and Machine Learning

Andreas Bender

Dr.

→ Group Bernd Bischl
 Statistical Learning and Data Science

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Christian Müller

Prof. Dr.

Principal Investigator

Biomedical Statistics and Data Science

Abstract

Recommender Systems (RS) pervade many aspects of our everyday digital life. Proposed to work at scale, state-of-the-art RS allow the modeling of thousands of interactions and facilitate highly individualized recommendations. Conceptually, many RS can be viewed as instances of statistical regression models that incorporate complex feature effects and potentially non-Gaussian outcomes. Such structured regression models, including time-aware varying coefficients models, are, however, limited in their applicability to categorical effects and inclusion of a large number of interactions. Here, we propose Factorized Structured Regression (FaStR) for scalable varying coefficient models. FaStR overcomes limitations of general regression models for large-scale data by combining structured additive regression and factorization approaches in a neural network-based model implementation. This fusion provides a scalable framework for the estimation of statistical models in previously infeasible data settings. Empirical results confirm that the estimation of varying coefficients of our approach is on par with state-of-the-art regression techniques, while scaling notably better and also being competitive with other time-aware RS in terms of prediction performance. We illustrate FaStR’s performance and interpretability on a large-scale behavioral study with smartphone user data.

inproceedings

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ECML-PKDD 2022

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Grenoble, France, Sep 19-23, 2022.

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Authors

D. Rügamer • A. Bender • S. Wiegrebe • D. Racek • B. Bischl • C. L. Müller • C. Stachl

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Links

DOI

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Research Areas

 A1 | Statistical Foundations & Explainability

 C2 | Biology