Accurately modeling users' evolving preferences from sequential interactions remains a central challenge in recommender systems. Recent studies emphasize the importance of capturing multiple latent intents underlying user behaviors. However, existing methods often fail to effectively exploit collective intent signals shared across users and items, leading to information isolation and limited robustness. Meanwhile, current contrastive learning approaches struggle to construct views that are both semantically consistent and sufficiently discriminative. In this work, we propose BIPCL, an end-to-end Bilateral Intent-enhanced, Embedding Perturbation-based Contrastive Learning framework. BIPCL explicitly integrates multi-intent signals into both item and sequence representations via a bilateral intent-enhancement mechanism. Specifically, shared intent prototypes on the user and item sides capture collective intent semantics distilled from behaviorally similar entities, which are subsequently integrated into representation learning. This design alleviates information isolation and improves robustness under sparse supervision. To construct effective contrastive views without disrupting temporal or structural dependencies, BIPCL injects bounded, direction-aware perturbations directly into structural item embeddings. On this basis, BIPCL further enforces multi-level contrastive alignment across interaction- and intent-level representations. Extensive experiments on benchmark datasets demonstrate that BIPCL consistently outperforms state-of-the-art baselines, with ablation studies confirming the contribution of each component.

翻译：从序列交互中准确建模用户动态变化的偏好仍是推荐系统的核心挑战。近期研究强调了捕捉用户行为背后多潜在意图的重要性。然而，现有方法通常未能有效利用跨用户和物品共享的集体意图信号，导致信息隔离与鲁棒性不足。同时，当前对比学习方法难以构建在语义上既一致又具有足够区分度的视图。本文提出BIPCL——一种端到端的双边意图增强、基于嵌入扰动的对比学习框架。BIPCL通过双边意图增强机制，将多意图信号显式整合至物品与序列表征中。具体而言，用户侧与物品侧的共享意图原型从行为相似实体中提炼集体意图语义，并将其融入表征学习。该设计缓解了信息隔离问题，并在稀疏监督下提升鲁棒性。为在不破坏时序或结构依赖的前提下构建有效对比视图，BIPCL将具有方向感知的有界扰动直接注入结构化物品嵌入。在此基础上，BIPCL进一步在交互级与意图级表征上实施多层级对比对齐。在基准数据集上的广泛实验表明，BIPCL持续优于最先进的基线方法，消融研究亦验证了各组件贡献。