PULSE用社交信号构建用户表示，减少一半参数却更准
PULSE: Socially-Aware User Representation Modeling Toward Parameter-Efficient Graph Collaborative Filtering
- 用社交关系生成用户表示，不为每个用户学独立嵌入
- 参数量减半，冷启动到活跃用户全场景表现最优
- 适合资源受限场景的高效推荐系统研发
基于图的社交推荐（SocialRec）通过图神经网络捕捉用户-物品交互中的多跳协同信号，将社交网络信息融入协同过滤以增强用户表征。然而现有方法因需为所有用户和物品显式学习嵌入，导致参数量庞大、计算成本高且难以扩展。本文提出PULSE（Parameter-efficient User representation Learning with Social Knowledge），通过从社交有意义信号中构建用户表示，无需为每个用户创建可学习嵌入，从而将参数规模降低高达50%。在时间与内存效率方面，该方法在不同交互稀疏度下（从冷启动到高活跃用户）均超越13个主流基线，实现最先进的性能。
原文摘要 · Abstract (English)
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.
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