通过捕捉用户行为间的协同效应，提升多行为推荐精度。
SWGCN: Synergy Weighted Graph Convolutional Network for Multi-Behavior Recommendation
- 按行为细粒度加权交互，动态调整不同操作的重要性。
- 在淘宝数据集上，点击率和排序质量分别提升112.49%和156.36%。
- 适合需要精准理解用户多行为意图的推荐系统开发者。
多行为推荐范式通过利用浏览等次要行为来预测主要转化（如购买）。然而，现有图模型常忽略跨行为协同信号及个体行为强度。为此，我们提出协同加权图卷积网络（SWGCN），包含两个新组件：目标偏好加权器，用于自适应调整各行为内用户-物品交互权重；协同对齐任务，借助辅助偏好评估器引导训练，优先学习更反映真实偏好的协同信号。在Taobao、IJCAI和Beibei三个公开数据集上的全面测试表明，SWGCN在淘宝数据集上相对提升了112.49%（命中率）和156.36%（归一化折损累计增益），并在其他数据集上保持一致优势，验证了其鲁棒性与泛化能力。代码已开源，可通过https://github.com/FangdChen/SWGCN获取。
原文摘要 · Abstract (English)
Multi-behavior recommendation paradigms have emerged to capture diverse user activities, forecasting primary conversions (e.g., purchases) by leveraging secondary signals like browsing history. However, current graph-based methods often overlook cross-behavioral synergistic signals and fine-grained intensity of individual actions. Motivated by the need to overcome these shortcomings, we introduce Synergy Weighted Graph Convolutional Network (SWGCN). SWGCN introduces two novel components: a Target Preference Weigher, which adaptively assigns weights to user-item interactions within each behavior, and a Synergy Alignment Task, which guides its training by leveraging an Auxiliary Preference Valuator. This task prioritizes interactions from synergistic signals that more accurately reflect user preferences. The performance of our model is rigorously evaluated through comprehensive tests on three open-source datasets, specifically Taobao, IJCAI, and Beibei. On the Taobao dataset, SWGCN yields relative gains of 112.49% and 156.36% in terms of Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG), respectively. It also yields consistent gains on IJCAI and Beibei, confirming its robustness and generalizability across various datasets. Our implementation is open-sourced and can be accessed via https://github.com/FangdChen/SWGCN.
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