Can we design effective recommender systems free from users, IDs, and GNNs? Recommender systems are central to personalized content delivery across domains, with top-K item recommendation being a fundamental task to retrieve the most relevant items from historical interactions. Existing methods rely on entrenched design conventions, often adopted without reconsideration, such as storing per-user embeddings (user-dependent), initializing features from raw IDs (ID-dependent), and employing graph neural networks (GNN-dependent). These dependencies incur several limitations, including high memory costs, cold-start and over-smoothing issues, and poor generalization to unseen interactions. In this work, we propose AlphaFree, a novel recommendation method free from users, IDs, and GNNs. Our main ideas are to infer preferences on-the-fly without user embeddings (user-free), replace raw IDs with language representations (LRs) from pre-trained language models (ID-free), and capture collaborative signals through augmentation with similar items and contrastive learning, without GNNs (GNN-free). Extensive experiments on various real-world datasets show that AlphaFree consistently outperforms its competitors, achieving up to around 40% improvements over non-LR-based methods and up to 5.7% improvements over LR-based methods, while significantly reducing GPU memory usage by up to 69% under high-dimensional LRs.

翻译：我们能否设计出无需用户、ID与图神经网络的有效推荐系统？推荐系统是个性化内容分发的核心，其中Top-K物品推荐作为基础任务，旨在从历史交互中检索最相关的物品。现有方法依赖于根深蒂固的设计惯例，这些惯例常被直接沿用而未加反思，例如存储每用户嵌入向量（用户依赖型）、从原始ID初始化特征（ID依赖型）以及采用图神经网络（GNN依赖型）。这些依赖关系带来了若干局限，包括高内存成本、冷启动与过度平滑问题，以及对未见交互的泛化能力不足。本文提出AlphaFree，一种无需用户、ID与图神经网络的新型推荐方法。我们的核心思路是：无需用户嵌入向量即可动态推断偏好（用户无关型）；用预训练语言模型生成的语言表征替代原始ID（ID无关型）；通过相似物品增强与对比学习捕捉协同信号，无需图神经网络（GNN无关型）。在多个真实数据集上的大量实验表明，AlphaFree始终优于现有方法，相较于非语言表征方法提升最高约40%，相较于语言表征方法提升最高5.7%，同时在高维语言表征下将GPU内存使用量显著降低最高达69%。