Cross-Domain Sequential Recommendation (CDSR) methods aim to address the data sparsity and cold-start problems present in Single-Domain Sequential Recommendation (SDSR). Existing CDSR methods typically rely on overlapping users, designing complex cross-domain modules to capture users' latent interests that can propagate across different domains. However, their propagated informative information is limited to the overlapping users and the users who have rich historical behavior records. As a result, these methods often underperform in real-world scenarios, where most users are non-overlapping (cold-start) and long-tailed. In this research, we introduce a new CDSR framework named Information Maximization Variational Autoencoder (\textbf{\texttt{IM-VAE}}). Here, we suggest using a Pseudo-Sequence Generator to enhance the user's interaction history input for downstream fine-grained CDSR models to alleviate the cold-start issues. We also propose a Generative Recommendation Framework combined with three regularizers inspired by the mutual information maximization (MIM) theory \cite{mcgill1954multivariate} to capture the semantic differences between a user's interests shared across domains and those specific to certain domains, as well as address the informational gap between a user's actual interaction sequences and the pseudo-sequences generated. To the best of our knowledge, this paper is the first CDSR work that considers the information disentanglement and denoising of pseudo-sequences in the open-world recommendation scenario. Empirical experiments illustrate that \texttt{IM-VAE} outperforms the state-of-the-art approaches on two real-world cross-domain datasets on all sorts of users, including cold-start and tailed users, demonstrating the effectiveness of \texttt{IM-VAE} in open-world recommendation.

翻译：跨域序列推荐方法旨在解决单域序列推荐中存在的**数据稀疏性**与**冷启动**问题。现有的跨域序列推荐方法通常依赖于重叠用户，设计复杂的跨域模块以捕获用户可在不同领域间传播的潜在兴趣。然而，这些方法传播的信息性信息仅限于重叠用户以及具有丰富历史行为记录的用户。因此，在现实场景中，当大多数用户为非重叠（冷启动）和长尾用户时，这些方法往往表现不佳。在本研究中，我们提出了一种名为**信息最大化变分自编码器**的新跨域序列推荐框架。在此框架中，我们建议使用**伪序列生成器**来增强用户的交互历史输入，以供下游细粒度跨域序列推荐模型使用，从而缓解冷启动问题。我们还提出了一个**生成式推荐框架**，并结合了三个受互信息最大化理论启发的正则化器，以捕获用户跨域共享的兴趣与特定领域兴趣之间的语义差异，并解决用户实际交互序列与生成的伪序列之间的信息差距。据我们所知，本文是首个在开放世界推荐场景中考虑伪序列的信息解耦与去噪的跨域序列推荐工作。实证实验表明，**IM-VAE** 在两个真实世界的跨域数据集上，对于包括冷启动和长尾用户在内的各类用户，均优于现有最先进方法，证明了 **IM-VAE** 在开放世界推荐中的有效性。