Neural Collaborative Recommendation Algorithm Based on Attention Mechanism and Knowledge Graph

被引：0
作者：
Zhang, Chuang [1 ,2 ]
Wang, Wei [1 ,2 ,3 ]
Du, Yuxuan [1 ,2 ,3 ]
Zheng, Xiaoli [1 ,2 ]
He, Tingting [1 ,2 ]
机构：
[1] School of Information & Electrical Engineering, Hebei University of Engineering, Hebei, Handan
[2] Hebei Key Laboratory of Security & Protection Information Sensing & Processing, Hebei University of Engineering, Hebei, Handan
[3] School of Internet of Things Engineering, Jiangnan University, Jiangsu, Wuxi
来源：
Computer Engineering and Applications | 2023年 / 59卷 / 22期
关键词：
attention mechanism; collaborative filtering; knowledge graph; neural network;
D O I：
10.3778/j.issn.1002-8331.2207-0314
中图分类号：
学科分类号：
摘要：
At present, the traditional recommendation algorithm based on collaborative filtering has poor performance in the face of sparse data and cold start. However, the recommendation system assisted by knowledge graph can effectively alleviate this problem. Supplemented by attention mechanism, a neural collaborative recommendation algorithm combining attention mechanism and knowledge graph is designed. Then the attention mechanism is used to learn and aggregate the higher-order potential relationship information in the knowledge graph. At the same time, the user’s final preference is obtained through gated recurrent neural network training based on the user’s long-term and short-term interest preferences. Finally, the collaborative filtering method is used to generate the recommendation list. Through experiments on MovieLens-1M and Amazon-Book datasets, the recommended recall rate, accuracy rate, hit rate and NDCG evaluation indicators are all improved, which validates the effectiveness of the proposed method. © 2023 The Author(s).
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页码：111 / 120
页数：9
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