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.

翻译：多行为推荐范式旨在捕捉多样化的用户活动，通过利用浏览历史等次要信号来预测主要转化行为（如购买）。然而，当前基于图的方法往往忽略了跨行为的协同信号以及个体行为的细粒度强度。为克服这些不足，我们提出了协同加权图卷积网络（SWGCN）。SWGCN引入了两个新颖组件：目标偏好加权器，用于自适应地为每种行为内的用户-物品交互分配权重；以及协同对齐任务，通过利用辅助偏好评估器来指导模型训练。该任务优先处理能更准确反映用户偏好的协同信号交互。我们在三个开源数据集（淘宝、IJCAI和贝贝）上进行了全面测试，严格评估了模型的性能。在淘宝数据集上，SWGCN在命中率（HR）和归一化折损累计增益（NDCG）方面分别实现了112.49%和156.36%的相对提升。在IJCAI和贝贝数据集上也取得了一致的增益，证实了其在不同数据集上的鲁棒性和泛化能力。我们的实现已开源，可通过 https://github.com/FangdChen/SWGCN 访问。