As the final stage of recommender systems, re-ranking presents ordered item lists to users that best match their interests. It plays such a critical role and has become a trending research topic with much attention from both academia and industry. Recent advances of re-ranking are focused on attentive listwise modeling of interactions and mutual influences among items to be re-ranked. However, principles to guide the learning process of a re-ranker, and to measure the quality of the output of the re-ranker, have been always missing. In this paper, we study such principles to learn a good re-ranker. Two principles are proposed, including convergence consistency and adversarial consistency. These two principles can be applied in the learning of a generic re-ranker and improve its performance. We validate such a finding by various baseline methods over different datasets.
View on arXiv@article{li2025_2504.04188,
title={ Towards Principled Learning for Re-ranking in Recommender Systems },
author={ Qunwei Li and Linghui Li and Jianbin Lin and Wenliang Zhong },
journal={arXiv preprint arXiv:2504.04188},
year={ 2025 }
}