计算机科学 ›› 2023, Vol. 50 ›› Issue (3): 49-64.doi: 10.11896/jsjkx.220700108
• 知识图谱赋能的知识工程:理论、技术与系统专题 • 上一篇 下一篇
于健1,4,5, 赵满坤1,4,5, 高洁1,4,5, 王聪源1,4,5, 李亚蓉2,4,5, 张文彬3,4,5
YU Jian1,4,5, ZHAO Mankun1,4,5, GAO Jie1,4,5, WANG Congyuan1,4,5, LI Yarong2,4,5, ZHANG Wenbin3,4,5
摘要： 跨项目社会推荐是一种将社交关系整合到推荐系统中的方法。社会化推荐中包含用户-项目交互图和社交网络图,用户是连接这两个图的桥梁,其表示学习对提升社会化推荐的性能至关重要。然而,现有方法主要使用用户或项目的静态属性和社交网络中的显式朋友关系来进行表示学习,用户和项目交互的时序信息及隐式朋友关系未得到充分利用。因此,在社会化推荐中,如何有效利用时序信息和社交信息成为重要的研究课题之一。文中通过建模用户的隐式朋友和项目的社交属性,提出了一种新颖的基于高阶和时序特征的图神经网络社会化推荐算法(Graph Neural Networks Social Recommendation Based on High-order and Temporal Features)模型,简称HTGSR。HTGSR首先利用门控递归单元对基于项目的用户表征进行建模,以反映用户的近期动态偏好,并定义一个高阶建模单元来提取用户的高阶连通特征,挖掘用户的隐式朋友信息;其次利用注意力机制获取基于社交关系的用户表征;然后提出不同的项目社交网络的构建方式,并利用注意力机制来获取项目表征;最后将用户和项目的潜在表征输入到多层感知机,完成用户对项目的评分预测。在两个数据集上进行详细的实验,并将实验结果与多种类型的推荐算法进行比较,结果表明HTGSR模型在两个数据集上的效果均较优。
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