Multi-Margin Loss: Proposal and Application in Recommender Systems

arXiv:2405.04614v1 Announce Type: new
Abstract: 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 …

abstract application arxiv collaborative collaborative filtering components cs.ir cs.lg deep learning deep learning techniques filtering guide information interactions loss nature recommender systems systems through type vast