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{ODCRec}, which integrates representation learning into an \underline{O}nline \underline{D}ifferentiable \underline{C}lustering framework for \underline{Rec}ommendation. Specifically, we encode users' behavior sequences and initialize the cluster centers as differentiable 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 ODCRec method. Code is available at: https://github.com/yueliu1999/ELCRec.

翻译：挖掘用户意图在序列推荐中扮演着关键角色。近期提出的ICLRec方法通过对比学习与聚类技术提取用户潜在意图。尽管该方法展现出有效性，但其复杂的交替优化过程存在两大问题：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，对全数据集执行聚类操作会限制大规模工业数据的可扩展性。针对上述挑战，我们提出名为\underline{ODCRec}的新型意图学习方法，该方法将表示学习集成到面向推荐的\underline{在线} \underline{可微} \underline{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微网络参数。同时设计聚类损失函数，引导网络区分不同聚类中心，并促使相似样本向其对应聚类中心靠拢。这使得推荐与聚类能够通过小批量数据实现同步优化。此外，我们利用学习到的聚类中心作为表示学习的自监督信号，进一步提升推荐性能。在公开基准与工业数据上的大量实验验证了ODCRec方法的优越性、有效性与高效性。相关代码见：https://github.com/yueliu1999/ELCRec。