# Generating Artificial Core Users for Interpretable Condensed Data

arXiv: https://arxiv.org/abs/2102.03674 · HTML: https://mlsanity.com/paper/2102.03674/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 6 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Learning based Recommender System: A Survey and New Perspectives](https://mlsanity.com/paper/1707.07435/) (2017)
- [Collaborative Deep Learning for Recommender Systems](https://mlsanity.com/paper/1409.2944/) (2014)
- [Provable Inductive Matrix Completion](https://mlsanity.com/paper/1306.0626/) (2013)
- [Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks](https://mlsanity.com/paper/1611.00454/) (2016)
- [Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow](https://mlsanity.com/paper/1803.01233/) (2018)
- [Adversarial Recommendation: Attack of the Learned Fake Users](https://mlsanity.com/paper/1809.08336/) (2018)

## Abstract (arXiv)

Recent work has shown that in a dataset of user ratings on items there exists a group of Core Users who hold most of the information necessary for recommendation. This set of Core Users can be as small as 20 percent of the users. Core Users can be used to make predictions for out-of-sample users without much additional work. Since Core Users substantially shrink a ratings dataset without much loss of information, they can be used to improve recommendation efficiency. We propose a method, combining latent factor models, ensemble boosting and K-means clustering, to generate a small set of Artificial Core Users (ACUs) from real Core User data. Our ACUs have dense rating information, and improve the recommendation performance of real Core Users while remaining interpretable.