融合多时间尺度和特征加强的知识图谱推荐模型

针对现有的知识图谱推荐模型没有考虑到用户的周期特征以及待推荐项目会对用户近期兴趣产生影响的问题，提出一种融合多时间尺度和特征加强的知识图谱推荐模型（MTFE）。首先，采用长短期记忆（LSTM）网络在不同时间尺度上挖掘用户的周期特征并融入到用户表示中；然后，通过注意力机制挖掘待推荐项目中与用户近期特征相关性较强的特征，将其加强后融入项目表示中；最后，通过评分函数计算用户对待推荐项目的评分。在真实数据集Last.FM、MovieLens-1M和MovieLens-20M上把所提模型和个性化实体推荐（PER）、协同知识嵌入（CKE）、LibFM、RippleNet、知识图卷积网络（KGCN）、协同知识感知注意网络（CKAN）等知识图谱推荐模型进行对比。实验结果表明，在三个数据集上MTFE相较于表现最优的对比模型的F1性能分别提升了0.78、1.63和1.92个百分点，AUC指标在三个数据集上分别提升了3.94、2.73和1.15个百分点。可见，所提模型相较于对比图谱推荐模型有更好的推荐效果。

Knowledge graph recommendation model with multiple time scales and feature enhancement

Aiming at the problems that the existing knowledge graph recommendation models do not consider the periodic features of the user and the items to be recommended will affect the recent interests of the user， a knowledge graph recommendation model with Multiple Time scales and Feature Enhancement （MTFE） was proposed. Firstly， Long Short-Term Memory （LSTM） network was used to mine the user’s periodic features on different time scales and integrate them into user representation. Then， attention mechanism was used to mine the features strongly correlated with the user’s recent features in the items to be recommended and integrate them into the item representation after enhancement. Finally， the scoring function was used to calculate user’s ratings of items to be recommended. The proposed model was compared with PER（Personalized Entity Recommendation）， CKE（Collaborative Knowledge base Embedding）， LibFM， RippleNet， KGCN（Knowledge Graph Convolutional Network）， CKAN（Collaborative Knowledge-aware Attentive Network） knowledge graph recommendation models on real datasets Last.FM， MovieLens-1M and MovieLens-20M. Experimental results show that compared with the model with the best prediction performance， MTFE model has the F1 value improved by 0.78 percentage points， 1.63 percentage points and 1.92 percentage points and the Area Under Curve of ROC （AUC）metric improved by 3.94 percentage points， 2.73 percentage points and 1.15 percentage points on three datasets respectively. In summary， compared with comparative knowledge graph recommendation models， the proposed knowledge graph recommendation model has better recommendation effect.