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Topic-aware Intention Network for Explainable Recommendation with Knowledge Enhancement

文献类型：期刊论文

作者Li, Qiming2,3; Zhang, Zhao2,4; Zhuang, Fuzhen5,6,7; Xu, Yongjun; Li, Chao1,2,4
刊名ACM TRANSACTIONS ON INFORMATION SYSTEMS
出版日期2023-10-01
卷号41期号:4页码:23
ISSN号1046-8188
关键词Knowledge graph recommender system topic model
DOI10.1145/3579993
英文摘要Recently, recommender systems based on knowledge graphs (KGs) have become a popular research direction. Graph neural network (GNN) is the key technology of KG-based recommendation systems. However, existing GNNs have a significant flaw: They cannot explicitly model users' intent in recommendations. Intent plays an essential role in users' behaviors. For example, users may first generate an intent to purchase a certain group of items and then select a specific item from the group based on their preferences. Therefore, explicitly modeling intent has a positive significance for improving recommendation performance and providing explanations for recommendations. In this article, we propose a new model called Topic-aware Intention Network (TIN) for explainable recommendations with KGs. TIN models user representations from both preference and intent views. Specifically, we design a relational attention graph neural network to selectively aggregate information in KG to learn user preferences, and we propose a knowledge-enhanced topic model to learn user intent, which is viewed as topics hidden in user behavior sequences. Finally, we obtain the user representation by fusing user preference and intent through an attention network. The experimental results show that our proposed model outperforms the state-of-the-art methods and can generate reasonable explanations for the recommendation results.
资助项目National Key Research and Development Program of China[2021ZD0113602] ; National Natural Science Foundation of China[62176014] ; National Natural Science Foundation of China[62206266] ; Fundamental Research Funds for the Central Universities ; China Postdoctoral Science Foundation[2021M703273]
WOS研究方向Computer Science
语种英语
出版者ASSOC COMPUTING MACHINERY
WOS记录号WOS:001068685300011
源URL[http://119.78.100.204/handle/2XEOYT63/21127]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Zhao; Li, Chao
作者单位1.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100191, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Zhejiang Lab, Hangzhou 311121, Peoples R China
5.Beihang Univ, Inst Artificial Intelligence, 37 Xueyuan Rd, Beijing 100191, Peoples R China
6.Zhongguancun Lab, Beijing, Peoples R China
7.Beihang Univ, Sch Comp Sci, SKLSDE, 37 Xueyuan Rd, Beijing 100191, Peoples R China
推荐引用方式
GB/T 7714		 Li, Qiming,Zhang, Zhao,Zhuang, Fuzhen,et al. Topic-aware Intention Network for Explainable Recommendation with Knowledge Enhancement[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS,2023,41(4):23.
APA Li, Qiming,Zhang, Zhao,Zhuang, Fuzhen,Xu, Yongjun,&Li, Chao.(2023).Topic-aware Intention Network for Explainable Recommendation with Knowledge Enhancement.ACM TRANSACTIONS ON INFORMATION SYSTEMS,41(4),23.
MLA Li, Qiming,et al."Topic-aware Intention Network for Explainable Recommendation with Knowledge Enhancement".ACM TRANSACTIONS ON INFORMATION SYSTEMS 41.4(2023):23.

入库方式： OAI收割

来源：计算技术研究所

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