Abstract
Recent advancements in diffusion models have shown promising results in sequential recommendation (SR). However, current diffusion-based methods still exhibit two key limitations. First, they implicitly model the diffusion process for target item embeddings rather than the discrete target item itself, leading to inconsistency in the recommendation process. Second, existing methods rely on either implicit or explicit conditional diffusion models, limiting their ability to fully capture the context of user behavior and leading to less robust target item embeddings. In this paper, we propose the Dual Conditional Diffusion Models for Sequential Recommendation (DCRec), introducing a discrete-to-continuous sequential recommendation diffusion framework. Our framework introduces a complete Markov chain to model the transition from the reversed target item representation to the discrete item index, bridging the discrete and continuous item spaces for diffusion models and ensuring consistency with the diffusion framework. Building on this framework, we present the Dual Conditional Diffusion Transformer (DCDT) that incorporates the implicit conditional and the explicit conditional for diffusion-based SR. Extensive experiments on public benchmark datasets demonstrate that DCRec outperforms state-of-the-art methods.
Abstract (translated)
近期，扩散模型在序列推荐（SR）方面的进展显示出有希望的结果。然而，当前基于扩散的方法仍然存在两个关键限制。首先，它们对目标项目嵌入进行隐式建模，而不是直接针对离散的目标项目本身，导致了推荐过程的一致性问题。其次，现有方法依赖于隐式或显式的条件扩散模型，这限制了它们捕捉用户行为上下文的能力，并导致目标项目的嵌入不够鲁棒。在本文中，我们提出了用于序列推荐的双条件扩散模型（DCRec），引入了一个离散到连续的序列推荐扩散框架。我们的框架通过引入一个完整的马尔可夫链来模拟从逆向目标项目表示到离散项目索引的转换过程，将离散和连续项目空间连接起来，并确保与扩散框架的一致性。基于这一框架，我们提出了双条件扩散变换器（DCDT），该模型结合了隐式条件和显式条件，用于基于扩散的SR。在公共基准数据集上的广泛实验表明，DCRec优于现有最先进的方法。
URL
https://arxiv.org/abs/2410.21967
