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物品列表来满足用户需求。在多阶段推荐系统中，重排序通过建模物品间的列表内关联发挥着关键作用。重排序的核心挑战在于从排列组合空间中探索最优序列。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，评估器则基于估计的列表式评分筛选出最佳序列。生成器至关重要，而生成模型非常适合承担生成器功能。当前的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具有挑战性。为解决这些问题，我们提出了一种用于推荐重排序的非自回归生成模型（NAR4Rec），旨在提升效率与效果。针对训练样本稀疏和候选集动态变化等挑战，我们引入了匹配模型。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序列与不可行序列。此外，为克服非自回归模型在目标物品依赖建模方面的不足，我们引入对比解码以捕捉这些物品间的关联性。大量离线实验验证了NAR4Rec相较于最先进重排序方法的优越性能。在线A/B测试表明，NAR4Rec显著提升了用户体验。目前，NAR4Rec已在拥有超过3亿日活跃用户的流行视频应用快手（Kuaishou）中完成全面部署。