计算机科学 ›› 2021, Vol. 48 ›› Issue (4): 104-110.doi: 10.11896/jsjkx.200800027
刘志鑫, 张泽华, 张杰
LIU Zhi-xin, ZHANG Ze-hua, ZHANG Jie
摘要： 推荐系统是当前数据挖掘领域的研究热点,海量数据的涌现促使多源信息融合的推荐方法得到极大的关注。但是,现有的基于异质信息融合的推荐方法在进行特征表示时往往忽略了用户和项目之间的交互信息以及元路径之间的相互影响。因此,考虑到属性节点嵌入和结构元路径的不同视角,提出了一种多层次图注意力的网络推荐方法。该方法通过构建不同的元路径,将多源信息网络结构粒化为多个独立的粗粒度网络,然后基于图注意力机制结合局部节点属性嵌入,来分别学习用户和项目的潜在特征,最终给出融合后的细粒度网络推荐。在现实大规模数据集上进行横向和纵向评测,实验结果表明该方法能够有效地提升推荐性能。
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