基于知识图谱的双端知识感知图卷积推荐模型

Knowledge Graph-based Recommendation Model with Bipartite Knowledge Aware GCN

针对现有基于知识图谱的推荐模型仅从用户或项目一端进行特征提取,从而缺乏对另一端的特征提取的问题,提出一种基于知识图谱的双端知识感知图卷积推荐模型.首先,对于用户、项目及知识图谱中的实体进行随机初始化表征得到初始特征表示;接着,采用基于用户和项目的知识感知注意力机制同时从用户、项目两端在知识图谱中进行特征提取;其次,使用图卷积网络采用不同的聚合方式聚合知识图谱传播过程中的特征信息并预测点击率;最后,为了验证模型的有效性,在Last.FM和Book-Crossing两个公开数据集上与4个基线模型进行对比实验.在Last.FM数据集上,AUC和F1分别比最优的基线模型提升了4.4%、3.8%,ACC提升了1.1%.在Book-Crossing数据集上,AUC和F1分别提升了1.5%、2.2%,ACC提升了1.4%.实验结果表明,本文的模型在AUC、F1和ACC指标上比其他的基线模型具有更好的鲁棒性.

To address the problem that existing knowledge graph-based recommendation models only perform feature extraction from one end of users or items,missing the feature extraction from the other end,a bipartite knowledge-aware graph convolution recommendation model based on knowledge graph is proposed.First,the initial feature representation is obtained by random initialization characterization of users,items and entities in the knowledge graph;then,a user and item-based knowledge-aware attention mechanism is used to simultaneously extract features from both users and items in the knowledge graph;next,a graph convolutional network is used to aggregate feature information in the knowledge graph propagation process using different aggregation methods and predict the click-through rate;finally,the effectiveness of the model is verified by comparing it with four baseline models on two publicly available datasets,Last.FM and Book-Crossing.On the Last.FM dataset,AUC and F1 improve by 4.4% and 3.8% respectively,and ACC improves by 1.1%,compared with the optimal baseline model.On the Book-Crossing dataset,AUC and F1 improve by 1.5%and 2.2%respectively,and ACC improves by 1.4%.The experimental results show that the model in this study has better robustness than other baseline models in AUC,F1 and ACC metrics.