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Rügamer, D.* ; Bender, A.* ; Wiegrebe, S.* ; Racek, D.* ; Bischl, B.* ; Müller, C.L. ; Stachl, C.*

Factorized Structured Regression for Large-Scale Varying Coefficient Models.

In:. Berlin [u.a.]: Springer, 2023. 20-35 (Lect. Notes Comput. Sc. ; 13717 LNAI)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
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.
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Publication type Article: Conference contribution
Corresponding Author
Keywords Generalized Additive Models ; Neural Networks ; Recommender Systems ; Tensor Regression ; Time-varying Effects
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 13717 LNAI, Issue: , Pages: 20-35 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute of Computational Biology (ICB)
Grants German Federal Ministry of Education and Research (BMBF)