针对知识图谱推荐算法用户端和项目端建模程度不均且模型复杂度较高等问题, 提出融合知识图谱和轻量图卷积网络的推荐算法. 在用户端, 利用用户相似性生成邻居集合, 将用户及其相似用户的交互记录在知识图谱上多次迭代传播, 增强用户特征表示. 在项目端, 将知识图谱中实体嵌入传播, 挖掘与用户喜好相关的项目信息; 接着, 利用轻量图卷积网络聚合邻域特征获得用户和项目的特征表示, 同时采用注意力机制将邻域权重融入实体, 增强节点的嵌入表示; 最后, 预测用户和项目之间的评分. 实验表明, 在Book-Crossing数据集上, 相较于最优基线, AUC和ACC分别提高了1.8%和2.3%. 在Yelp2018数据集上, AUC和ACC分别提高了1.2%和1.4%. 结果证明, 该模型与其他基准模型相比有较好的推荐性能.
Given the uneven modeling degree between the user and project sides of the recommendation algorithms for knowledge graphs as well as high model complexity, a recommendation algorithm that integrates knowledge graph and lightweight graph convolutional network is proposed. On the user side, neighbor sets are generated based on user similarity, and the interaction records of users and their similar users are iteratively propagated on the knowledge graph for many times to enhance the representation of user features. On the project side, the entity on the knowledge graph is embedded and propagated to mine the project information related to user preferences. Then, the lightweight graph convolutional network is adopted to aggregate neighborhood features to obtain the feature representations of users and projects. At the same time, the attention mechanism is employed to incorporate neighborhood weights into the entities to enhance node embedding representation. Finally, the ratings between the user and the project are predicted. Experiments show that on the Book-Crossing dataset, compared with the optimal baseline, AUC and ACC are improved by 1.8% and 2.3%, respectively. On the Yelp2018 dataset, AUC and ACC are improved by 1.2% and 1.4%, respectively. The results demonstrate that the proposed model has better recommendation performance compared with other benchmark models.