Generating Artificial Core Users for Interpretable Condensed Data

TL;DR

This paper introduces a method to generate Artificial Core Users using latent factor models, boosting, and clustering, which enhances recommendation efficiency and interpretability by condensing user data.

Contribution

The paper presents a novel approach to create Artificial Core Users that improve recommendation performance and interpretability compared to using real Core Users.

Findings

Artificial Core Users improve recommendation accuracy.

ACUs retain interpretability and dense rating information.

Method reduces dataset size with minimal information loss.

Abstract

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.