Recommender systems guide users through vast amounts of information by
suggesting items based on their predicted preferences. Collaborative
filtering-based deep learning techniques have regained popularity due to their
straightforward nature, relying only on user-item interactions. Typically,
these systems consist of three main components: an interaction module, a loss
function, and a negative sampling strategy. Initially, researchers focused on
enhancing performance by developing complex interaction modules. However, there
has been a recent shift toward refining loss functions and negative sampling
strategies. This shift has led to an increased interest in contrastive
learning, which pulls similar pairs closer while pushing dissimilar ones apart.
Contrastive learning involves key practices such as heavy data augmentation,
large batch sizes, and hard-negative sampling, but these also bring challenges
like high memory demands and under-utilization of some negative samples. The
proposed Multi-Margin Loss (MML) addresses these challenges by introducing
multiple margins and varying weights for negative samples. This allows MML to
efficiently utilize not only the hardest negatives but also other non-trivial
negatives, offering a simpler yet effective loss function that outperforms more
complex methods, especially when resources are limited. Experiments on two
well-known datasets demonstrated that MML achieved up to a 20% performance
improvement compared to a baseline contrastive loss function when fewer number
of negative samples are used.

多边界损失（MML）是一种简单而有效的损失函数，通过引入多个边界和变化的负样本权重，解决了资源有限时的挑战，并在两个知名数据集上实验证明，在使用更少的负样本时，MML 相对于基准对比损失函数取得了高达 20％的性能提升。