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}.

中文翻译：

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揭示选择性状态空间模型在终身顺序推荐中的能力

顺序推荐器已广泛应用于各种在线服务中，旨在从用户的顺序交互中模拟用户的动态兴趣。随着用户越来越多地参与在线平台，产生了大量的终生用户行为序列。然而，现有的顺序推荐模型通常很难处理这种终身序列。主要挑战来自计算复杂性和捕获序列内远程依赖性的能力。最近，出现了一种具有选择机制（即Mamba）的状态空间模型。在这项工作中，我们研究了 Mamba 在终身顺序推荐（即长度>=2k）方面的性能。更具体地说，我们利用 Mamba 模块有选择地对终身用户序列进行建模。我们进行了大量的实验来评估代表性序列推荐模型在终身序列设置中的性能。对两个真实世界数据集的实验证明了 Mamba 的优越性。我们发现 RecMamba 实现了与代表性模型相当的性能，同时显着减少了大约 70% 的训练时间和 80% 的内存成本。代码和数据可在 \url{https://github.com/nan Cheng58/RecMamba} 获取。