  Implicit feedback is frequently used for developing personalized
recommendation services due to its ubiquity and accessibility in real-world
systems. In order to effectively utilize such information, most research adopts
the pairwise ranking method on constructed training triplets (user, positive
item, negative item) and aims to distinguish between positive items and
negative items for each user. However, most of these methods treat all the
training triplets equally, which ignores the subtle difference between
different positive or negative items. On the other hand, even though some other
works make use of the auxiliary information (e.g., dwell time) of user
behaviors to capture this subtle difference, such auxiliary information is hard
to obtain. To mitigate the aforementioned problems, we propose a novel training
framework named Triplet Importance Learning (TIL), which adaptively learns the
importance score of training triplets. We devise two strategies for the
importance score generation and formulate the whole procedure as a bilevel
optimization, which does not require any rule-based design. We integrate the
proposed training procedure with several Matrix Factorization (MF)- and Graph
Neural Network (GNN)-based recommendation models, demonstrating the
compatibility of our framework. Via a comparison using three real-world
datasets with many state-of-the-art methods, we show that our proposed method
outperforms the best existing models by 3-21\% in terms of Recall@k for the
top-k recommendation.
