  Personalized recommendation relies on user historical behaviors to provide
user-interested items, and thus seriously struggles with the data sparsity
issue. A powerful positive item augmentation is beneficial to address the
sparsity issue, while few works could jointly consider both the accuracy and
diversity of these augmented training labels. In this work, we propose a novel
model-agnostic Diversified self-distillation guided positive augmentation
(DivSPA) for accurate and diverse positive item augmentations. Specifically,
DivSPA first conducts three types of retrieval strategies to collect
high-quality and diverse positive item candidates according to users' overall
interests, short-term intentions, and similar users. Next, a self-distillation
module is conducted to double-check and rerank these candidates as the final
positive augmentations. Extensive offline and online evaluations verify the
effectiveness of our proposed DivSPA on both accuracy and diversity. DivSPA is
simple and effective, which could be conveniently adapted to other base models
and systems. Currently, DivSPA has been deployed on multiple widely-used
real-world recommender systems.
