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Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network

文献类型：期刊论文

作者Sang, Lei1,2,3; Xu, Min2; Qian, Shengsheng4; Wu, Xindong3,5
刊名NEUROCOMPUTING
出版日期2021-09-24
卷号454页码:417-429
ISSN号0925-2312
关键词Recommendation system Knowledge Graph Relational Path Embedding Neural Collaborative Filtering Residual Recurrent Network
DOI10.1016/j.neucom.2021.03.053
通讯作者Xu, Min(Min.Xu@uts.edu.au)
英文摘要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.
WOS关键词RECOMMENDATION
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]
WOS研究方向Computer Science
语种英语
出版者ELSEVIER
WOS记录号WOS:000672468800015
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/45292]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
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.

入库方式： OAI收割

来源：自动化研究所

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