# Factorized Structured Regression for Large-Scale Varying Coefficient Models

arXiv: https://arxiv.org/abs/2205.13080 · HTML: https://mlsanity.com/paper/2205.13080/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 9 of 9)

- [RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms](https://mlsanity.com/paper/2011.01731/) (2020)
- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [On the Difficulty of Evaluating Baselines: A Study on Recommender Systems](https://mlsanity.com/paper/1905.01395/) (2019)
- [Higher-Order Factorization Machines](https://mlsanity.com/paper/1607.07195/) (2016)
- [Towards a Better Understanding of Linear Models for Recommendation](https://mlsanity.com/paper/2105.12937/) (2021)
- [Sequence-Aware Factorization Machines for Temporal Predictive Analytics](https://mlsanity.com/paper/1911.02752/) (2019)
- [Dynamic Tensor Recommender Systems](https://mlsanity.com/paper/2003.05568/) (2020)
- [Interaction-aware Factorization Machines for Recommender Systems](https://mlsanity.com/paper/1902.09757/) (2019)
- [Explainable Recommendation Systems by Generalized Additive Models with Manifest and Latent Interactions](https://mlsanity.com/paper/2012.08196/) (2020)

## Built on by (most cited first, 3 of 3)

- [A New PHO-rmula for Improved Performance of Semi-Structured Networks](https://mlsanity.com/paper/2306.00522/) (2023)
- [Scalable Higher-Order Tensor Product Spline Models](https://mlsanity.com/paper/2402.01090/) (2024)
- [LIT-LVM: Structured Regularization for Interaction Terms in Linear Predictors using Latent Variable Models](https://mlsanity.com/paper/2506.15492/) (2025)

## Abstract (arXiv)

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.