RØROS: Building a Responsive Online Recommender System via Meta-Gradients Updating

In the era of information explosion, users of online services are urgently waiting for timely and effective recommendations. In this paper, we present the first study on the responsiveness aspect of recommender system and present Responsive Online RecOmmender System (RØROS) based on Meta-Gradients Update (MGU) techniques, which helps improve the recommendation quality for both existing and new users when the system only observes a limited number of in-coming interactions. Technically, we propose a new sampling strategy to enable the recommender model to update itself in the unit of user-centered interaction graphs, which helps balance the responsiveness to different types of users. For model updating, we propose an MGU-based strategy to effectively capture users’ current short-term preference, which hence improve RØROS’s responsiveness to users. Mean-while, RØROS also maintains users’ long-term preference with a preference memory module. Extensive experiments and case studies on standard benchmarks validate the responsiveness of our novel recommender system design, which we hope would foster future studies in this new direction.