Topic-aware Intention Network for Explainable Recommendation with Knowledge Enhancement

doi:10.1145/3579993

	Topic-aware Intention Network for Explainable Recommendation with Knowledge Enhancement
	Li, Qiming 1,2; Zhang, Zhao 1,3; Zhuang, Fuzhen 4,5,6; Xu, Yongjun ; Li, Chao 1,3,7
	2023-10-01
发表期刊	ACM TRANSACTIONS ON INFORMATION SYSTEMS
ISSN	1046-8188
卷号	41 期号:4 页码:23
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.
关键词	Knowledge graph recommender system topic model
DOI	10.1145/3579993
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:001068685300011
出版者	ASSOC COMPUTING MACHINERY
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21128
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 100191, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Zhejiang Lab, Hangzhou 311121, Peoples R China 4.Beihang Univ, Inst Artificial Intelligence, 37 Xueyuan Rd, Beijing 100191, Peoples R China 5.Zhongguancun Lab, Beijing, Peoples R China 6.Beihang Univ, Sch Comp Sci, SKLSDE, 37 Xueyuan Rd, Beijing 100191, Peoples R China 7.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100190, 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.