  A choice of optimization objective is immensely pivotal in the design of a
recommender system as it affects the general modeling process of a user's
intent from previous interactions. Existing approaches mainly adhere to three
categories of loss functions: pairwise, pointwise, and setwise loss functions.
Despite their effectiveness, a critical and common drawback of such objectives
is viewing the next observed item as a unique positive while considering all
remaining items equally negative. Such a binary label assignment is generally
limited to assuring a higher recommendation score of the positive item,
neglecting potential structures induced by varying preferences between other
unobserved items. To alleviate this issue, we propose a novel method that
extends original objectives to explicitly leverage the different levels of
preferences as relative orders between their scores. Finally, we demonstrate
the superior performance of our method compared to baseline objectives.
