SPR: Supervised Personalized Ranking Based on Prior Knowledge for Recommendation

@article{Yang2022SPRSP,
  title={SPR: Supervised Personalized Ranking Based on Prior Knowledge for Recommendation},
  author={Chun Yang and Shicai Fan},
  journal={ArXiv},
  year={2022},
  volume={abs/2207.03197}
}
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… 

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