In a multi-stage recommendation system, reranking plays a crucial role by
modeling the intra-list correlations among items.The key challenge of reranking
lies in the exploration of optimal sequences within the combinatorial space of
permutations. Recent research proposes a generator-evaluator learning paradigm,
where the generator generates multiple feasible sequences and the evaluator
picks out the best sequence based on the estimated listwise score. Generator is
of vital importance, and generative models are well-suited for the generator
function. Current generative models employ an autoregressive strategy for
sequence generation. However, deploying autoregressive models in real-time
industrial systems is challenging. Hence, we propose a Non-AutoRegressive
generative model for reranking Recommendation (NAR4Rec) designed to enhance
efficiency and effectiveness. To address challenges related to sparse training
samples and dynamic candidates impacting model convergence, we introduce a
matching model. Considering the diverse nature of user feedback, we propose a
sequence-level unlikelihood training objective to distinguish feasible from
unfeasible sequences. Additionally, to overcome the lack of dependency modeling
in non-autoregressive models regarding target items, we introduce contrastive
decoding to capture correlations among these items. Extensive offline
experiments on publicly available datasets validate the superior performance of
our proposed approach compared to the existing state-of-the-art reranking
methods. Furthermore, our method has been fully deployed in a popular video app
Kuaishou with over 300 million daily active users, significantly enhancing
online recommendation quality, and demonstrating the effectiveness and
efficiency of our approach.

在多阶段推荐系统中，重新排名通过建模项之间的内部列表相关性发挥着关键作用。在重新排名中的关键挑战在于在排列组合空间中探索最优序列。最近的研究提出了一个生成器 - 评估器学习范式，其中生成器生成多个可行序列，评估器基于估计的列表得分选择出最佳序列。生成器是至关重要的，而生成模型非常适合生成器函数。然而，在实时工业系统中部署自回归模型是具有挑战性的。因此，我们提出了一种用于重新排名推荐的非自回归生成模型 (NAR4Rec)，旨在提高效率和效果。为了解决与稀疏训练样本和动态候选项对模型收敛的影响相关的挑战，我们引入了匹配模型。考虑到用户反馈的多样性，我们提出了一种序列级别的非可能性训练目标，以区分可行序列和不可行序列。此外，为了克服非自回归模型中关于目标项的依赖建模不足的问题，我们引入了对比解码来捕捉这些项之间的相关性。对公开可用数据集进行的大量离线实验验证了我们所提出方法与现有最先进的重新排名方法相比的卓越性能。此外，我们的方法已在拥有超过 3 亿日活跃用户的热门视频应用快手上完全部署，显著提高了在线推荐的质量，展示了我们方法的有效性和效率。