提出两个原则提升推荐系统重排序模型性能。
Towards Principled Learning for Re-ranking in Recommender Systems
- 引入收敛一致性和对抗一致性两个学习原则。
- 在多个数据集上显著提升重排序模型效果。
- 适合关注推荐系统优化的研究者与工程师。
作为推荐系统最后一环，重排序负责生成最符合用户兴趣的项目列表，其重要性日益凸显。当前研究聚焦于通过注意力机制建模项目间的交互与相互影响。然而，指导重排序器学习过程及评估输出质量的原则长期缺失。本文提出两个新原则：收敛一致性与对抗一致性，适用于通用重排序器的学习，能有效提升性能。我们在多种基线方法和不同数据集上验证了该方法的有效性。
原文摘要 · Abstract (English)
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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