基于融合元路径的图神经网络协同过滤算法

传统的协同过滤算法没有充分考虑用户和商品的交互信息,且面临数据稀疏、冷启动等问题,造成了推荐系统的结果不准确.在本文中提出了一种新的推荐算法,即基于融合元路径的图神经网络协同过滤算法.该算法首先由二部图嵌入用户和商品的历史互动,并通过多层神经网络传播获取用户和商品的高阶特征;然后基于元路径的随机游走来获取异质信息网络中的潜在语义信息;最后将用户和商品的高阶特征和潜在特征融合并做评分预测.实验结果表明,基于融合元路径的图神经网络协同过滤算法比传统的推荐算法有明显提升.

Collaborative Filtering Algorithm of Graph Neural Network Based on Fusion Meta-Path

The traditional collaborative filtering algorithms do not fully consider the user-item interaction information and face problems such as data sparseness or cold start, which results in inaccurate results of the recommendation system. For this reason, we propose a new recommendation algorithm, which is a collaborative filtering algorithm of graph neural network based on fusion meta-path. To be specific, first, the user-item historical interactions are embedded by a bipartite graph and the high-level features of users and items are obtained through multi-layer neural network propagation. Then, latent semantic information in the heterogeneous information network is acquired according to the random walk of meta-paths. Finally, the high-level features and latent features of users and items are combined for scoring prediction. The experimental results show that compared with the traditional recommendation algorithms, the proposed algorithm has been significantly improved.