General recommender systems deliver personalized services by learning user and item representations, with the central challenge being how to capture latent user preferences. However, representations derived from sparse interactions often fail to comprehensively characterize user behaviors, thereby limiting recommendation effectiveness. Recent studies attempt to enhance user representations through sophisticated modeling strategies ($e.g.,$ intent or language modeling). Nevertheless, most works primarily concentrate on model interpretability instead of representation optimization. This imbalance has led to limited progress, as representation optimization is crucial for recommendation quality by promoting the affinity between users and their interacted items in the feature space, yet remains largely overlooked. To overcome these limitations, we propose DIAURec, a novel representation learning framework that unifies intent and language modeling for recommendation. DIAURec reconstructs representations based on the prototype and distribution intent spaces formed by collaborative and language signals. Furthermore, we design a comprehensive representation optimization strategy. Specifically, we adopts alignment and uniformity as the primary optimization objectives, and incorporates both coarse- and fine-grained matching to achieve effective alignment across different spaces, thereby enhancing representational consistency. Additionally, we further introduce intra-space and interaction regularization to enhance model robustness and prevent representation collapse in reconstructed space representation. Experiments on three public datasets against fifteen baseline methods show that DIAURec consistently outperforms state-of-the-art baselines, fully validating its effectiveness and superiority.

翻译：通用推荐系统通过学习用户和物品表示来提供个性化服务，其核心挑战在于如何捕捉用户潜在偏好。然而，从稀疏交互中推导出的表示往往难以全面描述用户行为，从而限制了推荐效果。近期研究尝试通过复杂的建模策略（如意图建模或语言建模）增强用户表示，但大多数工作主要关注模型可解释性而非表示优化。这种失衡导致进展有限——因为表示优化通过促进特征空间中用户与其交互物品的亲和性，对推荐质量至关重要，却长期被忽视。为克服这些局限，我们提出DIAURec，一种统一意图建模与语言建模的新型表示学习框架。DIAURec基于协同信号与语言信号形成的原型意图空间和分布意图空间重构表示。此外，我们设计了一套全面的表示优化策略：具体而言，以对齐性和均匀性作为主要优化目标，并引入粗粒度与细粒度匹配实现跨空间有效对齐，从而增强表示一致性。进一步地，我们引入空间内正则化与交互正则化以增强模型鲁棒性，并防止重构空间中的表示坍缩。在三个公开数据集上对比十五种基线方法的实验表明，DIAURec持续优于最先进的基线模型，充分验证了其有效性与优越性。