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%。
Aiming at the problem that knowledge graph-driven graph neural network recommendation algorithm cannot learn the user and item representations at the same time,a graph neural network recommendation model that integrates user preferences is proposed.The model learns user and item representations from user’s perspective and entity’s perspective respectively.Firstly,the user’s perspective spreads user preferences in the knowledge graph based on user historical interaction records and enhances user representation.Secondly,the entity perspective gathers neighbor information of candidate entities through graph convolu-tional network to enrich the representation of the entity.At the same time,a hybrid layer is designed to capture high-level connectivity and hybrid hierarchical information from both the width and depth aspects to enhance the item representation.The enhanced user representation vector and item representation vector are input to the prediction function to predict the interaction probability.Finally,the fixed-size sampling method and phased training strategy are used to optimize the model.The click-through rate prediction experiment is conducted on the MovieLens-1 M data set,and the results show that,compared with the benchmark methods RippleNet and KGCN,its AUC increases by 1.7%and 2.3%respectively.
作者
熊中敏
舒贵文
郭怀宇
XIONG Zhong-min;SHU Gui-wen;GUO Huai-yu(College of Information,Shanghai Ocean University,Shanghai 201306,China)
出处
《计算机科学》
CSCD
北大核心
2022年第6期165-171,共7页
Computer Science
基金
国家自然科学基金(41501419)
上海市地方院校能力建设项目(19050502100)。
关键词
推荐系统
知识图谱
图神经网络
偏好传播
个性化推荐
Recommendation system
Knowledge graph
Graph neural network
Preference propagation
Personalized recommendation
