一种基于知识图谱共享信息的推荐模型

A RECOMMENDER MODEL BASED ON KNOWLEDGE GRAPHS SHARING INFORMATION

在结合知识图谱的推荐模型中,依赖用户历史行为和知识图谱嵌入得到的向量会丢失部分信息,使向量化表示不准确,且多数模型无法充分建模用户和物品的特征交互。针对上述问题,提出一种基于知识图谱共享信息的推荐模型ISRS。在知识图谱模块中,实体向量的训练需考虑当前三元组(head, relation, tail),即先建模head和relation的关系,再与物品共享信息;通过DeepFM建模用户和物品间的低阶、高阶特征交互。实验表明:该模型与主流推荐模型相比,在CTR预测和Top-K推荐场景下都有更优的表现。

In the recommendation model combined with the knowledge graph,the item vector obtained by user history behavior and knowledge graph embedding may lose some information,which makes the item vector representation inaccurate.In addition,most recommendation model cannot fully model the feature interaction between user and item.To solve the above problems,a recommender model based on knowledge graphs sharing information(ISRS)is proposed.In the knowledge graph module,the training of entity vector had to consider the current triad(head,relation,tail),which was modeling the relationship between head vector and relation vector and then sharing information with the item vector.The feature interactions of user and item were extracted by DeepFM layers,and the low-order and high-order interactions were modeled.The experiments demonstrate that ISRS model performs better in CTR prediction and Top-K recommendation,compared with other state-of-the-art methods.