Personalized Online Learning with Pseudo-Ground Truth

Abstract

Personalized online machine learning allows a very accurate modelling of individual behavior and demands. In particular, a system that dynamically adapts during runtime can initiate a continuous collaboration with its user where both alternately adjust to each other to maximize the system's utility. However, in application scenarios based on supervised learning it is often unclear how to obtain the required ground truth for such dynamic systems. In this paper, we focus on applications where a real-time classification of sequential data is crucial. Concretely, we propose to adapt an online personalized model solely based on pseudo-ground-truth information which is provided by another machine learning model. This model has the advantage to classify sequences in retrospective with a small delay and thus is able to achieve a higher performance than real-time systems. In particular, it is a pre-trained offline model, which means that no ground-truth information is necessary during runtime. We apply the proposal on the task of online action classification, for which the benefits of personalization have been recently emphasized.