  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
simplicity, using only 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 with techniques like
multi-layer perceptrons, transformers, or graph neural networks. However, there
has been a recent shift toward refining loss functions and negative sampling
strategies. This shift has 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. MML efficiently utilizes 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
showed MML achieved up to a 20\% performance improvement compared to a baseline
contrastive loss function with fewer negative samples.
