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：基于图的社交推荐作为图协同过滤的强大扩展，利用图神经网络从用户-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图协同过滤来丰富用户表示，从而整合来自社交关系的额外协同信号。然而，现有图协同过滤及基于图的社交推荐方法面临显著挑战：由于需要为所有用户和物品分配显式嵌入表示，它们产生高昂计算成本并受限于可扩展性。本研究提出PULSE（基于社交知识的参数高效用户表示学习框架），通过从具有社会意义的信号构建用户表示而非为每个用户创建显式可学习嵌入，以解决此局限性。与最轻量级图协同过滤基线相比，PULSE将参数量减少高达50%。除参数效率外，本方法通过时空高效的建模过程，在不同交互稀疏度（从冷启动用户到高活跃度用户）场景下超越13个图协同过滤及基于图的社交推荐基线，实现了最先进的性能表现。