Sequential Recommenders have been widely applied in various online services, aiming to model users' dynamic interests from their sequential interactions. With users increasingly engaging with online platforms, vast amounts of lifelong user behavioral sequences have been generated. However, existing sequential recommender models often struggle to handle such lifelong sequences. The primary challenges stem from computational complexity and the ability to capture long-range dependencies within the sequence. Recently, a state space model featuring a selective mechanism (i.e., Mamba) has emerged. In this work, we investigate the performance of Mamba for lifelong sequential recommendation (i.e., length>=2k). More specifically, we leverage the Mamba block to model lifelong user sequences selectively. We conduct extensive experiments to evaluate the performance of representative sequential recommendation models in the setting of lifelong sequences. Experiments on two real-world datasets demonstrate the superiority of Mamba. We found that RecMamba achieves performance comparable to the representative model while significantly reducing training duration by approximately 70% and memory costs by 80%. Codes and data are available at \url{https://github.com/nancheng58/RecMamba}.

翻译：序列推荐系统广泛应用于各类在线服务中，旨在从用户的历史交互序列中建模其动态兴趣。随着用户越来越多地参与在线平台，产生了海量的终身用户行为序列。然而，现有的序列推荐模型往往难以处理此类终身序列。主要挑战源于计算复杂度以及捕获序列中长期依赖关系的能力。近期，一种具备选择性机制的状态空间模型（即Mamba）应运而生。在本研究中，我们探究了Mamba在终身序列推荐（即序列长度>=2000）中的表现。具体而言，我们利用Mamba模块对终身用户序列进行选择性建模。通过大量实验，我们评估了代表性序列推荐模型在终身序列场景下的性能。在两个真实数据集上的实验证明了Mamba的优越性。我们发现，RecMamba在达到与代表性模型相当性能的同时，显著减少了约70%的训练时长和80%的内存消耗。代码与数据详见\url{https://github.com/nancheng58/RecMamba}。