Intent learning, which aims to learn users' intents for user understanding and item recommendation, has become a hot research spot in recent years. However, the existing methods suffer from complex and cumbersome alternating optimization, limiting the performance and scalability. To this end, we propose a novel intent learning method termed \underline{ELCRec}, by unifying behavior representation learning into an \underline{E}nd-to-end \underline{L}earnable \underline{C}lustering framework, for effective and efficient \underline{Rec}ommendation. Concretely, we encode users' behavior sequences and initialize the cluster centers (latent intents) as learnable neurons. Then, we design a novel learnable clustering module to separate different cluster centers, thus decoupling users' complex intents. Meanwhile, it guides the network to learn intents from behaviors by forcing behavior embeddings close to cluster centers. This allows simultaneous optimization of recommendation and clustering via mini-batch data. Moreover, we propose intent-assisted contrastive learning by using cluster centers as self-supervision signals, further enhancing mutual promotion. Both experimental results and theoretical analyses demonstrate the superiority of ELCRec from six perspectives. Compared to the runner-up, ELCRec improves NDCG@5 by 8.9\% and reduces computational costs by 22.5\% on Beauty dataset. Furthermore, due to the scalability and universal applicability, we deploy this method on the industrial recommendation system with 130 million page views and achieve promising results.

翻译：意图学习旨在通过理解用户意图以辅助用户建模与物品推荐，近年来已成为研究热点。然而，现有方法存在复杂繁琐的交替优化问题，限制了其性能与可扩展性。为此，我们提出一种名为 **ELCRec** 的新型意图学习方法，通过将行为表示学习统一纳入端到端可学习聚类框架，实现高效推荐。具体而言，我们对用户行为序列进行编码，并将聚类中心（潜在意图）初始化为可学习神经元。随后，我们设计一种新颖的可学习聚类模块，通过分离不同聚类中心来解耦用户复杂意图；同时，通过迫使行为嵌入向聚类中心靠近，引导网络从行为中学习意图。这使得推荐与聚类能够通过小批量数据同步优化。此外，我们利用聚类中心作为自监督信号，提出意图辅助对比学习，进一步增强两者的相互促进。实验与理论分析从六个维度证明了 ELCRec 的优越性。在 Beauty 数据集上，相比第二名方法，ELCRec 将 NDCG@5 提升 8.9%，计算成本降低 22.5%。由于该方法的可扩展性与普适性，我们已将其部署于拥有 1.3 亿页面浏览量的工业推荐系统中，并取得显著成效。