Towards Principled Learning for Re-ranking in Recommender Systems

• URL: http://arxiv.org/abs/2504.04188v1
• Date: Sat, 05 Apr 2025 14:14:36 GMT
• Title: Towards Principled Learning for Re-ranking in Recommender Systems
• Authors: Qunwei Li, Linghui Li, Jianbin Lin, Wenliang Zhong,
• Abstract summary: Two principles are proposed, including convergence consistency and adversarial consistency.<n>These two principles can be applied in the learning of a generic re-ranker and improve its performance.
• Score: 10.881339468480467
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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