Controllable Generative Model for Content Metadata Considering User Preferences

Abstract

In recent years, research on adoptation of generative models to content creation has begun to attract attention.In general, generative models set the ”reality” of the product as the objective function. However, since manycommercial contents (movies, music, games, etc.) are created to be appreciated by a larger number of users or aspecific target user group, the high reality alone is not enough for the content to be practical. Existing research hasproposed a generative model for content metadata that considers preferences of a targeted user groups, using dataof user ratings for the contents. However, even if, for example, a creator wants to create a movie of a specific genre,the existing model cannot control the generated content. In this study, we propose a novel model that extends theexisting model to control the generated content by using arbitrary metadata as input. In our experiments, we usedthe Internet Movie Database and generated actor candidates using the movie genre as input.