基于自适应图卷积注意力神经协同推荐算法

随着互联网的快速发展，推荐系统可以用来处理信息过载的问题。由于传统推荐系统的诸多问题导致其无法处理发掘隐藏信息，提出一种自适应图卷积注意力神经协同推荐算法（ANGCACF）。首先获取用户和项目交互图，通过图卷积神经网络自适应的聚合用户和项目特征信息；其次对用户和项目特征信息添加自适应扩充数据，以解决数据稀疏性，利用注意力机制对用户和项目特征信息及添加的自适应扩充数据重新分配权重；最后将得到的用户和项目特征表示使用基于矩阵分解的协同过滤的算法框架得出最终推荐结果。在MovieLens-1M、MovieLens-100K和 Amazon-baby三个公开数据集上的实验表明，该算法在推荐准确率、召回率、MRR、命中率和 NDCG 五个指标上均优于基线方法。

Collaborative filtering recommendation algorithm based on adaptive neural graph convolution attention neural network

With the rapid development of the Internet, the recommendation system can handle the problem of information overload. Due to many problems in traditional recommendation systems, they can''t handle the discovery of hidden information. This paper proposed an adaptive graph convolution attention neural collaborative filtering recommendation model(ANGCACF). Firstly, graph convolution neural network obtained the user-item interaction figure and adaptively aggregated the user-item feature information. Secondly, the model added adaptive extended data to solve the data sparsity for the user-item feature information and used the attention mechanism to redistribute the weight to the user-item feature information and the adaptive extended data. Finally, it obtained the final recommendation result by using the algorithm framework of collaborative filtering based on matrix decomposition. Experiments on MovieLens-1M, MovieLens-100K and Amazon-book show that the algorithm is superior to the baseline method in five indexes: precision, recall, MRR, hit and NDCG.