Learning with Limited Labels via Momentum Damped & Differentially Weighted Optimization

As deep learning-based models are deployed more widely in search & recommender systems, system designers often face the issue of gathering large amounts of well-annotated data to train such neural models. While most user-centric systems rely on interaction signals as implicit feedback to train models, such signals are often weak proxies of user satisfaction, as compared to (say) explicit judgments from users, which are prohibitively expensive to collect. In this paper, we consider the task of learning from limited labeled data, wherein we aim at jointly leveraging strong supervision data (e.g. explicit judgments) along with weak supervision data (e.g. implicit feedback or labels from the related task) to train neural models.