Sequential Recommendation (SR) plays a pivotal role in recommender systems by tailoring recommendations to user preferences based on their non-stationary historical interactions. Achieving high-quality performance in SR requires attention to both item representation and diversity. However, designing an SR method that simultaneously optimizes these merits remains a long-standing challenge. In this study, we address this issue by integrating recent generative Diffusion Models (DM) into SR. DM has demonstrated utility in representation learning and diverse image generation. Nevertheless, a straightforward combination of SR and DM leads to sub-optimal performance due to discrepancies in learning objectives (recommendation vs. noise reconstruction) and the respective learning spaces (non-stationary vs. stationary). To overcome this, we propose a novel framework called DimeRec (\textbf{Di}ffusion with \textbf{m}ulti-interest \textbf{e}nhanced \textbf{Rec}ommender). DimeRec synergistically combines a guidance extraction module (GEM) and a generative diffusion aggregation module (DAM). The GEM extracts crucial stationary guidance signals from the user's non-stationary interaction history, while the DAM employs a generative diffusion process conditioned on GEM's outputs to reconstruct and generate consistent recommendations. Our numerical experiments demonstrate that DimeRec significantly outperforms established baseline methods across three publicly available datasets. Furthermore, we have successfully deployed DimeRec on a large-scale short video recommendation platform, serving hundreds of millions of users. Live A/B testing confirms that our method improves both users' time spent and result diversification.

翻译：序列推荐在推荐系统中发挥着关键作用，它根据用户非平稳的历史交互记录，为其提供个性化的推荐。要在序列推荐中实现高质量的性能，需要同时关注物品表示和多样性。然而，设计一种能同时优化这两方面优点的序列推荐方法，一直是一个长期存在的挑战。在本研究中，我们通过将最新的生成扩散模型整合到序列推荐中来应对这一问题。扩散模型已在表示学习和多样化图像生成方面证明了其效用。然而，由于学习目标（推荐与噪声重构）和各自学习空间（非平稳与平稳）的差异，将序列推荐与扩散模型简单结合会导致次优性能。为了克服这一难题，我们提出了一个名为DimeRec（\textbf{Di}ffusion with \textbf{m}ulti-interest \textbf{e}nhanced \textbf{Rec}ommender）的新框架。DimeRec协同结合了一个引导提取模块和一个生成扩散聚合模块。引导提取模块从用户的非平稳交互历史中提取关键的平稳引导信号，而生成扩散聚合模块则采用以引导提取模块输出为条件的生成扩散过程，来重构并生成一致的推荐。我们的数值实验表明，在三个公开可用的数据集上，DimeRec显著优于已有的基线方法。此外，我们已成功将DimeRec部署在一个服务于数亿用户的大规模短视频推荐平台上。线上A/B测试证实，我们的方法同时提升了用户的使用时长和结果的多样性。