Towards Principled Learning for Re-ranking in Recommender Systems

2025-04-05

AI Summary

Re-ranking in recommender systems has long suffered from a lack of theoretical foundations and verifiable quality evaluation criteria. To address this, we propose two principled learning principles—convergence consistency and adversarial consistency—establishing, for the first time, an interpretable and generalizable theoretical basis for re-ranking modeling. Building upon these principles, we design a generic consistency regularization training framework that seamlessly integrates with mainstream listwise models (e.g., BERT4Rec, SetRank) without modifying their backbone architectures. Extensive experiments on multiple public benchmarks demonstrate consistent improvements in NDCG@10 by 1.2–3.7%, validating both the universality and effectiveness of our principles. This work fills a critical gap in the field by introducing formal, learnable constraints for re-ranking optimization.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Adversarial Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Lack of principles for re-ranker learning process
• Missing quality measurement for re-ranker output
• Need for consistent principles to improve re-ranker performance

Innovation

• Attentive listwise modeling for re-ranking
• Convergence consistency principle application
• Adversarial consistency principle integration