基于图注意力网络表示学习的协同过滤推荐算法

针对传统的基于模型的协同过滤推荐算法未能有效利用用户与项目的属性信息以及用户之间与项目之间的关系结构信息, 本文提出一种基于图注意力网络表示学习的协同过滤推荐算法. 该算法使用知识图谱表示节点的属性特征信息和节点间的关系结构信息, 并在用户和项目的同质网络上进行节点的图注意力网络表示学习, 得到用户和项目的网络嵌入特征表示, 最后构建融合网络嵌入信息的神经矩阵分解模型获得推荐结果. 本文在Movielens数据集上与相关算法进行对比实验, 实验证明该算法能优化模型的推荐性能, 提高推荐的召回率HR@K和归一化折损累计增益NDCG@K.

Collaborative Filtering Recommendation Algorithm Based on Graph Attention Network Representation Learning

Given that the traditional model-based collaborative filtering recommendation algorithm fails to effectively utilize the attributes of users and items and the relationship structures among users and items, this study proposes a collaborative filtering recommendation algorithm based onrepresentation learning with graph attention networks. The algorithm uses the knowledge graph to represent the attribute features of the nodes and the relationship structures among the nodes. Then, representation learning of nodes with graph attention networks is performed on the homogeneous networks of users and items to obtain their network embedding feature representations. Finally, a neural matrix factorization model integrating network embedding is constructed to obtain the recommendation results.This paper conducts comparative experiments with related algorithms on the Movielens dataset. Experiments show that the proposed algorithm can optimize the recommendation performance of the model and improve the recommended recall rate HR@K and the normalized discounted cumulative gain NDCG@K.