Shared-account usage is common on streaming and e-commerce platforms, where multiple users share one account. Existing shared-account sequential recommendation (SSR) methods often assume a fixed number of latent users per account, limiting their ability to adapt to diverse sharing patterns and reducing recommendation accuracy. Recent latent reasoning technique applied in sequential recommendation (SR) generate intermediate embeddings from the user embedding (e.g, last item embedding) to uncover users' potential interests, which inspires us to treat the problem of inferring the number of latent users as generating a series of intermediate embeddings, shifting from inferring preferences behind user to inferring the users behind account. However, the last item cannot be directly used for reasoning in SSR, as it can only represent the behavior of the most recent latent user, rather than the collective behavior of the entire account. To address this, we propose DisenReason, a two-stage reasoning method tailored to SSR. DisenReason combines behavior disentanglement stage from frequency-domain perspective to create a collective and unified account behavior representation, which serves as a pivot for latent user reasoning stage to infer the number of users behind the account. Experiments on four benchmark datasets show that DisenReason consistently outperforms all state-of-the-art baselines across four benchmark datasets, achieving relative improvements of up to 12.56\% in MRR@5 and 6.06\% in Recall@20.

翻译：共享账户使用在流媒体和电子商务平台中十分普遍，即多个用户共享一个账户。现有的共享账户序列推荐方法通常假设每个账户具有固定数量的潜在用户，这限制了其适应多样化共享模式的能力，并降低了推荐准确性。最近应用于序列推荐的潜在推理技术通过从用户嵌入（例如最后一项嵌入）生成中间嵌入来揭示用户的潜在兴趣，这启发我们将推断潜在用户数量的问题视为生成一系列中间嵌入的过程，从而将从推断用户背后的偏好转变为推断账户背后的用户。然而，在共享账户序列推荐中，最后一项不能直接用于推理，因为它仅能代表最近潜在用户的行为，而非整个账户的集体行为。为解决此问题，我们提出了DisenReason，一种专为共享账户序列推荐设计的两阶段推理方法。DisenReason结合了从频域视角出发的行为解耦阶段，以创建集体且统一的账户行为表示，该表示作为潜在用户推理阶段的枢纽，用于推断账户背后的用户数量。在四个基准数据集上的实验表明，DisenReason在四个基准数据集上始终优于所有最先进的基线方法，在MRR@5和Recall@20指标上分别实现了高达12.56%和6.06%的相对提升。