The recommendation algorithm based on knowledge graphs is at a relatively mature stage. However, there are still some problems in the recommendation of specific areas. For example, in the tourism field, selecting suitable tourist attraction attributes process is complicated as the recommendation basis for tourist attractions. In this paper, we propose the improved Attention Knowledge Graph Convolution Network model, named (Att-KGCN), which automatically discovers the neighboring entities of the target scenic spot semantically. The attention layer aggregates relatively similar locations and represents them with an adjacent vector. Then, according to the tourist's preferred choices, the model predicts the probability of similar spots as a recommendation system. A knowledge graph dataset of tourist attractions used based on tourism data on Socotra Island-Yemen. Through experiments, it is verified that the Attention Knowledge Graph Convolution Network has a good effect on the recommendation of tourist attractions and can make more recommendations for tourists' choices.

翻译：基于知识图谱的推荐算法已发展至相对成熟阶段，但在特定领域推荐中仍存在问题。例如在旅游领域，筛选合适的旅游景点属性作为推荐依据的过程较为复杂。本文提出改进的注意力知识图谱卷积网络模型（Att-KGCN），该模型能自动识别目标景点的语义邻近实体。注意力层对具有相似特征的场所进行聚合，并以邻接向量形式表示。随后，模型根据游客偏好选择，预测相似场所的推荐概率。基于也门索科特拉岛旅游数据构建的旅游景点知识图谱数据集，实验验证了注意力知识图谱卷积网络在旅游景点推荐中的良好效果，能够为游客提供更精准的推荐选择。