融合知识图谱表示学习的栈式自编码器推荐算法

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。

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

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.