# Deep Factorization Machines for Knowledge Tracing

arXiv: https://arxiv.org/abs/1805.00356 · HTML: https://mlsanity.com/paper/1805.00356/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2018.

## Built on by (most cited first, 2 of 2)

- [Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems](https://mlsanity.com/paper/2212.09920/) (2022)
- [Fair Knowledge Tracing in Second Language Acquisition](https://mlsanity.com/paper/2412.18048/) (2024)

## Abstract (arXiv)

This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning model of pairwise relationships between users, items, skills, and other entities considered. Our solution (AUC 0.815) hopefully managed to beat the logistic regression baseline (AUC 0.774) but not the top performing model (AUC 0.861) and reveals interesting strategies to build upon item response theory models.