  Personalized item ranking has been a crucial component contributing to the
performance of recommender systems. As a representative approach, pairwise
ranking directly optimizes the ranking with user implicit feedback by
constructing (\textit{user}, \textit{positive item}, \textit{negative item})
triplets. Several recent works have noticed that treating all triplets equally
may hardly achieve the best effects. They assign different importance scores to
negative items, user-item pairs, or triplets, respectively. However, almost all
the generated importance scores are groundless and hard to interpret, thus far
from trustworthy and transparent. To tackle these, we propose the
\textit{Triplet Shapley} -- a Shapely value-based method to measure the triplet
importance in an interpretable manner. Due to the huge number of triplets, we
transform the original Shapley value calculation to the Monte Carlo (MC)
approximation, where the guarantee for the approximation unbiasedness is also
provided. To stabilize the MC approximation, we adopt a control
covariates-based method. Finally, we utilize the triplet Shapley value to guide
the resampling of important triplets for benefiting the model learning.
Extensive experiments are conducted on six public datasets involving classical
matrix factorization- and graph neural network-based recommendation models.
Empirical results and subsequent analysis show that our model consistently
outperforms the state-of-the-art methods.
