论文标题
基于注意图的建议
Attention-Based Recommendation On Graphs
论文作者
论文摘要
图形神经网络（GNN）在不同任务中表现出色。但是，有一些关于GNN关于推荐系统的研究。 GCN作为一种GNN可以为图中的不同实体提取高质量的嵌入。在协作过滤任务中，核心问题是找出实体预测目标用户未来行为的信息。使用注意机制，我们可以在将基础数据建模为图形时，使GCN能够进行这样的分析。在这项研究中，我们提出了GAREC作为基于模型的推荐系统，该系统将注意机制以及推荐图上的空间GCN以及用于用户和项目提取嵌入的空间GCN。注意机制告诉GCN相关用户或项目应影响目标实体的最终表示。我们将GAREC与RMSE的某些基线算法进行了比较。提出的方法优于现有的基于模型的，非图形神经网络和不同Movielens数据集中的图形神经网络。
Graph Neural Networks (GNN) have shown remarkable performance in different tasks. However, there are a few studies about GNN on recommender systems. GCN as a type of GNNs can extract high-quality embeddings for different entities in a graph. In a collaborative filtering task, the core problem is to find out how informative an entity would be for predicting the future behavior of a target user. Using an attention mechanism, we can enable GCNs to do such an analysis when the underlying data is modeled as a graph. In this study, we proposed GARec as a model-based recommender system that applies an attention mechanism along with a spatial GCN on a recommender graph to extract embeddings for users and items. The attention mechanism tells GCN how much a related user or item should affect the final representation of the target entity. We compared the performance of GARec against some baseline algorithms in terms of RMSE. The presented method outperforms existing model-based, non-graph neural networks and graph neural networks in different MovieLens datasets.
