计算机科学 ›› 2022, Vol. 49 ›› Issue (6): 165-171.doi: 10.11896/jsjkx.210400276
熊中敏, 舒贵文, 郭怀宇
XIONG Zhong-min, SHU Gui-wen, GUO Huai-yu
摘要： 针对知识图谱驱动的图神经网络推荐算法无法同时学习用户和项目表示的问题,提出了融合用户偏好的图神经网络推荐模型,该模型分别从用户视角和实体视角学习用户和项目表示。首先,用户视角根据用户历史交互记录在知识图谱中传播用户偏好,增强用户表示;其次,实体视角通过图卷积网络聚集候选实体邻居信息以丰富实体的表示,同时设计一个混合层,分别从宽度和深度两个方面捕获高阶连通性和混合分层信息来增强项目表示,再将增强的用户表示向量和项目表示向量输入预测函数中,用于预测交互概率;最后,使用固定个数采样方法和阶段性训练策略优化模型的性能。在MovieLens-1M数据集上进行点击率预测实验,结果表明,所提模型的AUC与基准方法RippleNet和KGCN相比分别提升了1.7%和2.3%。
中图分类号:
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