  Users who come to recommendation platforms are heterogeneous in activity
levels. There usually exists a group of core users who visit the platform
regularly and consume a large body of content upon each visit, while others are
casual users who tend to visit the platform occasionally and consume less each
time. As a result, consumption activities from core users often dominate the
training data used for learning. As core users can exhibit different activity
patterns from casual users, recommender systems trained on historical user
activity data usually achieve much worse performance on casual users than core
users. To bridge the gap, we propose a model-agnostic framework L2Aug to
improve recommendations for casual users through data augmentation, without
sacrificing core user experience. L2Aug is powered by a data augmentor that
learns to generate augmented interaction sequences, in order to fine-tune and
optimize the performance of the recommendation system for casual users. On four
real-world public datasets, L2Aug outperforms other treatment methods and
achieves the best sequential recommendation performance for both casual and
core users. We also test L2Aug in an online simulation environment with
real-time feedback to further validate its efficacy, and showcase its
flexibility in supporting different augmentation actions.
