Bilateral Intent-Enhanced Sequential Recommendation with Embedding Perturbation-Based Contrastive Learning

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Modeling evolving user preferences from interaction sequences remains a core problem in sequential recommendation (SR). Recent work highlights intent learning for uncovering latent user motivations. Yet, existing methods either model intents within individual sequences or treat global intent signals as auxiliary supervision, limiting the explicit use of collective behavioral patterns and causing information isolation. Meanwhile, existing contrastive learning strategies are often costly and rely on suboptimal view construction, e.g., random sequence editing or weakly controlled model perturbations. We propose BIPCL, an end-to-end Bilateral Intent-enhanced, Embedding Perturbation-based Contrastive Learning framework. BIPCL integrates collective intent priors into both sequence- and item-side representations via bilateral intent enhancement. Shared intent prototypes capture collective semantics from behaviorally similar entities and inject them into representations, alleviating information isolation and improving robustness. It further induces a non-separable cross-intent interaction, providing complementary sequence–item matching signals beyond unilateral intent modeling. To construct effective contrastive views, we introduce an embedding perturbation-based paradigm that directly perturbs structural item embeddings, yielding bounded and discriminative views while preserving temporal and structural dependencies. Compatibility studies across multiple CL-based SR backbones demonstrate the effectiveness of this paradigm beyond BIPCL. Extensive experiments show that BIPCL consistently outperforms state-of-the-art baselines. All code and datasets are publicly available at https://github.com/ZINUX1998/BIPCL.