基于图神经网络与深度学习的商品推荐算法

基于图神经网络的推荐算法可以提取传统方法无法提取用户与商品之间的关联关系.目前此类算法大多忽略了用户和商品的评论数据中所存在的一般偏好.针对这一问题,提出了一种方法,在利用图神经网络提取关联关系的同时,利用深度学习提取评论的优势提取用户和商品的一般偏好,并进行特征融合来提升推荐效果.在四组公共数据集中进行了对比实验,使用召回率和归一化折损累计增益作为评价指标,并通过消融实验验证了方法的有效性.实验表明该方法比已有相关算法的效果更好.两种网络的特征融合对推荐效果有提升作用.

Item recommendation algorithm based on GNN and deep learning

The recommendation algorithm based on graph neural network can extract the association relationship between users and goods. Traditional methods can''t extract this relationship. At present, most of these algorithms ignore the general prefe-rences in the review data of users and products. In order to solve this problem, this paper proposed a new method. This method used the graph neural network to extract association relations, and the advantage of deep learning to extract the general preferences, and carried out feature fusion to improve the recommendation effect. This paper conducted comparative experiments and ablation experiments on four sets of public data sets to verify the effectiveness of the proposed method. The evaluation indexes include the recall rate and normalized discounted cumulative gain. Experiments show that this method is more effective than the existing algorithms. The feature fusion of the two networks can improve the recommendation effect.