融合增强协同信息和知识图谱信息的推荐模型

将知识图谱引入推荐系统，能一定程度解决数据稀疏和冷启动问题，但是往往忽略了高阶协同信息和不同协同信息的重要程度对探索用户潜在偏好的重要性，由此提出了一种融合增强协同信息和知识图谱信息的推荐模型（CIKG）。该模型首先利用用户和项目的历史交互数据，获取一阶协同信息和高阶协同信息，同时使用注意力机制捕获重要信息，得到增强协同信息，用来补充用户和项目的特征表示。其次通过将用户交互的项目与知识图谱中的实体对应，在知识图谱中执行传播操作，得到知识图谱信息，用于挖掘用户的偏好并且增强模型的可解释性。最后通过聚合器将增强协同信息和知识图谱信息结合得到用户和项目的最终表示，从而进行预测。在Last-fm和Book-crossing两个数据集上进行的实验结果表明CIKG相比其他对比的模型推荐效果有较大提升。

Recommendation model combining enhanced collaborative information and knowledge graph information

The recommender system uses knowledge graph can solve the problems of data sparsity and cold start, but it often ignores the importance of high-order collaborative information and different collaborative information to explore users'' potential preferences. Therefore, this paper proposed a recommendation model combining enhanced collaborative information and know-ledge graph information(CIKG). This model first used the historical interactive data of users and items to obtain first-order collaboration information and high-order collaboration information. At the same time, it used the attention mechanism to capture important information and obtain enhanced collaboration information to supplement the feature representation of users and items. Secondly, this model matched interacted items with the entities in the knowledge graph and performed the propagation in the knowledge graph. It could obtain knowledge graph information and used the information to obtain the user''s preferences and enhance the interpretability of the model. Finally, the aggregator combined the enhanced collaboration information and knowledge graph information to obtain the final representation of users and items, so as to make predictions. The experimental results on Last-fm and Book-crossing datasets show that CIKG has a great improvement over other comparative models.