The goal of a recommendation system is to model the relevance between each
user and each item through the user-item interaction history, so that maximize
the positive samples score and minimize negative samples. Currently, two
popular loss functions are widely used to optimize recommender systems: the
pointwise and the pairwise. Although these loss functions are widely used,
however, there are two problems. (1) These traditional loss functions do not
fit the goals of recommendation systems adequately and utilize prior knowledge
information sufficiently. (2) The slow convergence speed of these traditional
loss functions makes the practical application of various recommendation models
difficult.
To address these issues, we propose a novel loss function named Supervised
Personalized Ranking (SPR) Based on Prior Knowledge. The proposed method
improves the BPR loss by exploiting the prior knowledge on the interaction
history of each user or item in the raw data. Unlike BPR, instead of
constructing triples, the proposed SPR
constructs quadruples.
Although SPR is very simple, it is very effective. Extensive experiments show
that our proposed SPR not only achieves better recommendation performance, but
also significantly accelerates the convergence speed, resulting in a
significant reduction in the required training time