Reranking plays a crucial role in modern multi-stage recommender systems by rearranging the initial ranking list to model interplay between items. Considering the inherent challenges of reranking such as combinatorial searching space, some previous studies have adopted the evaluator-generator paradigm, with a generator producing feasible sequences and a evaluator selecting the best one based on estimated listwise utility. Inspired by the remarkable success of diffusion generative models, this paper explores the potential of diffusion models for generating high-quality sequences in reranking. However, we argue that it is nontrivial to take diffusion models as the generator in the context of recommendation. Firstly, diffusion models primarily operate in continuous data space, differing from the discrete data space of item permutations. Secondly, the recommendation task is different from conventional generation tasks as the purpose of recommender systems is to fulfill user interests. Lastly, real-life recommender systems require efficiency, posing challenges for the inference of diffusion models. To overcome these challenges, we propose a novel Discrete Conditional Diffusion Reranking (DCDR) framework for recommendation. DCDR extends traditional diffusion models by introducing a discrete forward process with tractable posteriors, which adds noise to item sequences through step-wise discrete operations (e.g., swapping). Additionally, DCDR incorporates a conditional reverse process that generates item sequences conditioned on expected user responses. Extensive offline experiments conducted on public datasets demonstrate that DCDR outperforms state-of-the-art reranking methods. Furthermore, DCDR has been deployed in a real-world video app with over 300 million daily active users, significantly enhancing online recommendation quality.

翻译：重排序通过重新排列初始排序列表以建模项目间的交互，在现代多阶段推荐系统中发挥着关键作用。考虑到重排序面临的组合搜索空间等固有挑战，部分先前研究采用了评估器-生成器范式，其中生成器产生可行序列，评估器基于估计的列表级效用选择最优序列。受扩散生成模型显著成功的启发，本文探索了扩散模型在重排序中生成高质量序列的潜力。然而，我们认为在推荐场景下将扩散模型作为生成器存在非平凡挑战。首先，扩散模型主要运行在连续数据空间中，与项目排列的离散数据空间存在差异。其次，推荐任务不同于传统生成任务，因为推荐系统的目的是满足用户兴趣。最后，现实推荐系统对效率有要求，这对扩散模型的推理构成了挑战。为克服这些挑战，我们提出了一种新颖的离散条件扩散重排序（DCDR）框架。DCDR通过引入具有可计算后验的离散前向过程扩展了传统扩散模型，该过程通过逐步离散操作（如交换）向项目序列添加噪声。此外，DCDR结合了条件反向过程，可生成以期望用户响应为条件的项目序列。在公开数据集上进行的大量离线实验表明，DCDR优于最先进的重排序方法。同时，DCDR已在拥有超过3亿日活跃用户的真实视频应用中部署，显著提升了在线推荐质量。