Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network

doi:10.1016/j.neucom.2021.03.053

	Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network
	Sang, Lei 1,2,3; Xu, Min 2; Qian, Shengsheng4 ; Wu, Xindong 3,5
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2021-09-24
卷号	454 页码:417-429
通讯作者	Xu, Min(Min.Xu@uts.edu.au)
摘要	Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem in recommender system. However, existing KG based recommendation methods mainly rely on handcrafted meta-path features or simple triple-level entity embedding, which cannot automatically capture entities' long-term relational dependencies for the recommendation. Specially, entity embedding learning is not properly designed to combine user item interaction information with KG context information. In this paper, a two-channel neural interaction method named Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network (KGNCF-RRN) is proposed, which leverages both long-term relational dependencies KG context and user-item interaction for recommendation. (1) For the KG context interaction channel, we propose Residual Recurrent Network (RRN) to construct context-based path embedding, which incorporates residual learning into traditional recurrent neural networks (RNNs) to efficiently encode the long-term relational dependencies of KG. The self-attention network is then applied to the path embedding to capture the polysemy of various user interaction behaviours. (2) For the user-item interaction channel, the user and item embeddings are fed into a newly designed two-dimensional interaction map. (3) Finally, above the two-channel neural interaction matrix, we employ a convolutional neural network to learn complex correlations between user and item. Extensive experimental results on three benchmark data sets show that our proposed approach outperforms existing state-of-the-art approaches for knowledge graph based recommendation. CO 2021 Published by Elsevier B.V.
关键词	Recommendation system Knowledge Graph Relational Path Embedding Neural Collaborative Filtering Residual Recurrent Network
DOI	10.1016/j.neucom.2021.03.053
关键词[WOS]	RECOMMENDATION
收录类别	SCI
语种	英语
资助项目	programs for Innovative Research Team in University of the Ministry of Education[IRT_17R32] ; National Key Research and Development Program of China[2016YFB1000901] ; National Natural Science Foundation of China[61673152] ; National Natural Science Foundation of China[91746209] ; National Natural Science Foundation of China[428]
项目资助者	programs for Innovative Research Team in University of the Ministry of Education ; National Key Research and Development Program of China ; National Natural Science Foundation of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000672468800015
出版者	ELSEVIER
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/45293
专题	多模态人工智能系统全国重点实验室_多媒体计算
通讯作者	Xu, Min
作者单位	1.Anhui Univ, Sch Comp Sci & Technol, Hefei 230000, Anhui, Peoples R China 2.Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia 3.Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 5.Hefei Univ Technol, Minist Educ, Key Lab Knowledge Engn Big Data, Hefei 230009, Peoples R China
推荐引用方式 GB/T 7714	Sang, Lei,Xu, Min,Qian, Shengsheng,et al. Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network[J]. NEUROCOMPUTING,2021,454:417-429.
APA	Sang, Lei,Xu, Min,Qian, Shengsheng,&Wu, Xindong.(2021).Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network.NEUROCOMPUTING,454,417-429.
MLA	Sang, Lei,et al."Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network".NEUROCOMPUTING 454(2021):417-429.