Graph-based social recommendation (SocialRec) has emerged as a powerful extension of graph collaborative filtering (GCF), which leverages graph neural networks (GNNs) to capture multi-hop collaborative signals from user-item interactions. These methods enrich user representations by incorporating social network information into GCF, thereby integrating additional collaborative signals from social relations. However, existing GCF and graph-based SocialRec approaches face significant challenges: they incur high computational costs and suffer from limited scalability due to the large number of parameters required to assign explicit embeddings to all users and items. In this work, we propose PULSE (Parameter-efficient User representation Learning with Social Knowledge), a framework that addresses this limitation by constructing user representations from socially meaningful signals without creating an explicit learnable embedding for each user. PULSE reduces the parameter size by up to 50% compared to the most lightweight GCF baseline. Beyond parameter efficiency, our method achieves state-of-the-art performance, outperforming 13 GCF and graph-based social recommendation baselines across varying levels of interaction sparsity, from cold-start to highly active users, through a time- and memory-efficient modeling process.

翻译：基于图的社交推荐（SocialRec）已成为图协同过滤（GCF）的重要扩展，其利用图神经网络（GNN）从用户-物品交互中捕获多跳协同信号。这些方法通过将社交网络信息融入GCF来丰富用户表征，从而整合来自社交关系的额外协同信号。然而，现有的GCF与基于图的SocialRec方法面临显著挑战：由于需要为所有用户和物品分配显式嵌入向量，它们计算成本高昂且可扩展性受限。本研究提出PULSE（基于社交知识的参数高效用户表征学习框架），该框架通过从具有社会意义的信号中构建用户表征，而无需为每个用户创建显式可学习嵌入，从而解决了这一局限性。与最轻量级的GCF基线相比，PULSE将参数量减少高达50%。除参数效率外，我们的方法通过时间与内存高效的建模过程，在从冷启动到高活跃度用户的不同交互稀疏度水平上，超越了13个GCF及基于图的社交推荐基线，实现了最先进的性能。