CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算
Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network
Sang, Lei1,2,3; Xu, Min2; Qian, Shengsheng4; Wu, Xindong3,5
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2021-09-24
Volume454Pages:417-429
Corresponding AuthorXu, Min(Min.Xu@uts.edu.au)
AbstractKnowledge 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.
KeywordRecommendation system Knowledge Graph Relational Path Embedding Neural Collaborative Filtering Residual Recurrent Network
DOI10.1016/j.neucom.2021.03.053
WOS KeywordRECOMMENDATION
Indexed BySCI
Language英语
Funding Projectprograms 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]
Funding Organizationprograms 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 Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000672468800015
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/45293
Collection模式识别国家重点实验室_多媒体计算
Corresponding AuthorXu, Min
Affiliation1.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
Recommended Citation
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.
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