Mining users' intents plays a crucial role in sequential recommendation. The recent approach, ICLRec, was introduced to extract underlying users' intents using contrastive learning and clustering. While it has shown effectiveness, the existing method suffers from complex and cumbersome alternating optimization, leading to two main issues. Firstly, the separation of representation learning and clustering optimization within a generalized expectation maximization (EM) framework often results in sub-optimal performance. Secondly, performing clustering on the entire dataset hampers scalability for large-scale industry data. To address these challenges, we propose a novel intent learning method called \underline{ELCRec}, which integrates representation learning into an \underline{E}nd-to-end \underline{L}earnable \underline{C}lustering framework for \underline{Rec}ommendation. Specifically, we encode users' behavior sequences and initialize the cluster centers as learnable network parameters. Additionally, we design a clustering loss that guides the networks to differentiate between different cluster centers and pull similar samples towards their respective cluster centers. This allows simultaneous optimization of recommendation and clustering using mini-batch data. Moreover, we leverage the learned cluster centers as self-supervision signals for representation learning, resulting in further enhancement of recommendation performance. Extensive experiments conducted on open benchmarks and industry data validate the superiority, effectiveness, and efficiency of our proposed ELCRec method. Code is available at: https://github.com/yueliu1999/ELCRec.

翻译：挖掘用户意图在序列推荐中起着关键作用。近期提出的ICLRec方法通过对比学习和聚类来提取用户潜在意图。尽管该方法表现出有效性，但现有方法受限于复杂繁琐的交替优化过程，导致两个主要问题：首先，在广义期望最大化框架中分离表示学习与聚类优化常导致次优性能；其次，对全数据集进行聚类阻碍了在大规模工业数据上的可扩展性。为解决这些挑战，我们提出一种新颖的意图学习方法——ELCRec，该方法将表示学习整合到面向推荐的端到端可学习聚类框架中。具体而言，我们对用户行为序列进行编码，并将聚类中心初始化为可学习的网络参数。此外，我们设计了一种聚类损失函数，引导网络区分不同聚类中心，并将相似样本拉近至各自聚类中心。这使得我们能够利用小批量数据同步优化推荐与聚类过程。进一步地，我们将学习到的聚类中心作为表示学习的自监督信号，从而进一步提升推荐性能。在公开基准和工业数据上的大量实验验证了所提出ELCRec方法的优越性、有效性和高效性。代码已开源：https://github.com/yueliu1999/ELCRec。