  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.
