融合知识图谱表示学习的栈式自编码器推荐算法

RECOMMENDATION ALGORITHM BASED ON REPRESENTATION LEARNING OF KNOWLEDGE GRAPH AND STACK AUTOENCODER

针对目前协同过滤推荐算法中数据稀疏和语义信息欠缺问题,提出一种融合知识图谱表示学习的栈式自编码器推荐算法(SAEKG-CF)。将评分矩阵作为栈式自编码器的输入,训练得到项目的隐性特征向量,并据此计算特征相似性矩阵;利用知识图谱表示学习算法将项目中的实体映射到低维向量空间,并计算出低维向量空间中实体间的语义相似性矩阵;将特征相似性矩阵与语义相似性矩阵相融合,得到融合相似性矩阵,进而依据最优融合相似性矩阵产生top-k推荐列表。实验结果表明,该算法能有效地同时解决数据稀疏与语义信息欠缺问题,提高推荐的准确率。

Aiming at the problem of data sparseness and lack of semantic information in the current collaborative filtering recommendation algorithm,a stacked autoencoder recommendation algorithm based on knowledge graph representation learning(SAEKG-CF)is proposed.It used the rating matrix as the input of stack self-encoder,trained the implicit feature vector of the project,and then calculated the feature similarity matrix;the knowledge graph representation learning algorithm was used to map the entities in the project to the low-dimensional vector space,and calculated semantic similarity matrix between entities;the feature similarity matrix was merged with the semantic similarity matrix to obtain a fusion similarity matrix;according to the optimal fusion similarity matrix,a top-k recommendation list was generated.The experimental results show that the proposed algorithm can effectively solve the problem of sparse data and lack of semantic information,and improve the accuracy of recommendation.