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: this https URL

通过 ELCRec 方法，我们将表示学习与聚类优化相结合，实现了推荐中端到端可学习的聚类框架，提高了推荐性能并克服了现有方法中的挑战。