Contemporary recommendation systems are designed to meet users' needs by delivering tailored lists of items that align with their specific demands or interests. 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. The 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. To address these issues, we propose a Non-AutoRegressive generative model for reranking Recommendation (NAR4Rec) designed to enhance efficiency and effectiveness. To tackle challenges such as sparse training samples and dynamic candidates, we introduce a matching model. Considering the diverse nature of user feedback, we employ a sequence-level unlikelihood training objective to differentiate feasible sequences from unfeasible ones. 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 validate the superior performance of NAR4Rec over state-of-the-art reranking methods. Online A/B tests reveal that NAR4Rec significantly enhances the user experience. Furthermore, NAR4Rec has been fully deployed in a popular video app Kuaishou with over 300 million daily active users.

翻译：当代推荐系统旨在通过提供符合用户特定需求或兴趣的定制化项目列表来满足用户需求。在多阶段推荐系统中，重排序通过建模项目间的列表内相关性发挥着关键作用。重排序的核心挑战在于探索排列组合空间中的最优序列。近期研究提出了一种生成器-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级分数选出最佳序列。生成器至关重要，而生成模型非常适合该功能。当前生成模型采用自回归策略进行序列生成，然而在实时工业系统中部署自回归模型颇具挑战。为解决这些问题，我们提出了一种用于重排序推荐的非自回归生成模型（NAR4Rec），旨在提升效率与有效性。为应对训练样本稀疏和候选动态变化等挑战，我们引入了一个匹配模型。考虑到用户反馈的多样性，我们采用序列级非似然训练目标来区分可行与不可行序列。此外，为解决非自回归模型中目标项目缺乏依赖建模的问题，我们引入对比解码来捕捉这些项目间的相关性。大量离线实验验证了NAR4Rec相较于最先进重排序方法的卓越性能。在线A/B测试表明NAR4Rec显著提升了用户体验。更进一步，NAR4Rec已在拥有超过3亿日活跃用户的流行视频应用快手全面部署。