Generating Artificial Core Users for Interpretable Condensed Data
This work is significant for researchers and practitioners in recommendation systems who need to reduce data size while maintaining or improving prediction accuracy.
This paper tackles the problem of efficiently representing user rating data for recommendations. They propose a method to generate a small set of Artificial Core Users (ACUs) that condense rating information, improving recommendation performance compared to using real Core Users.
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.