计算机科学 ›› 2023, Vol. 50 ›› Issue (1): 41-51.doi: 10.11896/jsjkx.220900255
郝敬宇, 文静轩, 刘华锋, 景丽萍, 于剑
HAO Jingyu, WEN Jingxuan, LIU Huafeng, JING Liping, YU Jian
摘要： 基于GCN的协同过滤模型通过用户物品交互二部图上的信息聚合过程生成用户节点和物品节点的表示,预测用户对物品的偏好。然而,这些模型大多没有考虑用户不同的交互意图,无法充分挖掘用户与物品之间的关系。已有的图解耦协同过滤模型建模了用户的交互意图,却忽略了图全局信息,没有考虑用户节点和物品节点的本质特征,造成表示语义不完整;并且由于受到模型迭代结构的影响,意图解耦学习的过程并不高效。针对上述问题,设计了结合全局信息的深度图解耦协同过滤模型G2DCF(Global Graph Disentangled Collaborative Filtering)。该模型构建了图全局通道和图解耦通道,分别学习节点的本质特征和意图特征;通过引入正交约束和表示独立性约束,使用户-物品的交互意图尽可能唯一防止意图退化,同时提高不同意图下表示的独立性,提升模型的解耦效果。对比已有的图协同过滤模型,G2DCF能更综合地刻画用户特征和物品特征。在3个公开数据集上进行了实验,结果表明G2DCF在多个评价指标上优于对比方法;分析了表示分布的表示独立性和表示均匀性,验证了模型的解耦效果;同时从收敛速度上进行了对比,验证了模型的有效性。
中图分类号:
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