Multi-Margin Loss: Proposal and Application in Recommender Systems

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.