融合知识图谱与协同过滤的推荐模型

Recommendation Model Fusing with Knowledge Graph and Collaborative Filtering

针对现有协同过滤推荐算法可解释性不高和基于内容推荐信息提取困难、推荐效率低等问题,提出一种融合知识图谱和协同过滤的混合推荐模型,其由知识图谱与深度学习结合模型RCKD和知识图谱与协同过滤结合模型RCKC构成。RCKD模型在获取知识图谱的推理路径后,利用TransE算法将路径嵌入为向量,并使用LSTM和soft attention机制捕获路径推理的语义,通过池化操作区分不同路径推理的重要性,经全连接层和sigmoid函数获得预测评分。RCKC模型根据知识图谱表示学习的语义相似性,利用协同过滤算法获得预测评分。按预测评分的准确度将两个模型相互融合,最终获得可解释的混合推荐模型。在MovieLens数据集上的实验结果表明,与RKGE、RippleN模型和经典协同过滤算法相比,该模型具有较好的推荐可解释性和较高的推荐准确率。

To address the problems of existing collaborative filtering recommendation algorithms,such as low interpretability and difficulty in information extraction based on content recommendation and low recommendation efficiency,this paper proposes a hybrid recommendation model fusing with knowledge graph and collaborative filtering.The model is composed of the RCKD model and the RCKC model,the former combining knowledge graph and deep learning,and the latter combining knowledge graph and collaborative filtering.After obtaining the inference path of knowledge graph,the RCKD model uses the TransE algorithm to embed the path into vector,and captures the semantics of path inference by using LSTM and the soft attention mechanism.Then the importance of different path inferences is distinguished through pooling operation,and the prediction score is obtained through the full connection layer and the sigmoid function.According to the semantic similarity of knowledge graph representation learning,the RCKC model uses the collaborative filtering algorithm to obtain the prediction score.The two models are fused with each other according to the accuracy of the prediction score,and finally the interpretable hybrid recommendation model is obtained.The experimental results on the MovieLens data set show that the proposed model has better recommendation interpretability and higher recommendation accuracy than the RKGE model,RippleN model and the classical collaborative filtering algorithms.