Knowledge graph driven recommendation model of graph neural network

doi:10.11772/j.issn.1001-9081.2020081254

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (7): 1865-1870.DOI: 10.11772/j.issn.1001-9081.2020081254

Special Issue: 人工智能

• Artificial intelligence • Previous Articles Next Articles

Knowledge graph driven recommendation model of graph neural network

LIU Huan^1,2, LI Xiaoge^1,2, HU Likun³, HU Feixiong³, WANG Penghua^1,2

1. School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an Shaanxi 710121, China;
2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing(Xi'an University of Posts and Telecommunications), Xi'an Shaanxi 710121, China;
3. Department of Intelligent Operation and Maintenance, Shenzhen Tencent Computer Systems Company Limited, Shenzhen Guangdong 518000, China

Received:2020-08-17 Revised:2020-12-24 Online:2021-07-10 Published:2021-01-22
Supported by:
This work is partially supported by the Innovation Capacity Support Project of Shaanxi Province (2019PT-12), the National Key Research and Development Program of China (2018YFB1403004).

基于知识图谱驱动的图神经网络推荐模型

刘欢^1,2, 李晓戈^1,2, 胡立坤³, 胡飞雄³, 王鹏华^1,2

1. 西安邮电大学计算机学院, 西安 710121;
2. 陕西省网络数据分析与智能处理重点实验室(西安邮电大学), 西安 710121;
3. 深圳腾讯计算机系统有限公司智能化运维部, 广东深圳 518000

通讯作者: 李晓戈
作者简介:刘欢(1995-),男,陕西咸阳人,硕士研究生,主要研究方向:自然语言处理、推荐系统;李晓戈(1962-),男,安徽合肥人,教授,博士,主要研究方向:自然语言处理、机器学习;胡立坤(1991-),男,湖北黄冈人,硕士,主要研究方向:智能化运维、数据挖掘;胡飞雄(1990-),男,江西九江人,主要研究方向:智能化运维、数据挖掘;王鹏华(1991-),男,陕西咸阳人,硕士,主要研究方向:自然语言处理、数据挖掘。
基金资助:
国家重点研发计划项目（2018YFB1403004）；陕西省创新能力支撑计划项目（2019PT-12）。

Abstract

Abstract: The abundant structure and association information contained in Knowledge Graph (KG) can not only alleviate the data sparseness and cold-start in the recommender systems, but also make personalized recommendation more accurately. Therefore, a knowledge graph driven end-to-end recommendation model of graph neural network, named KGLN, was proposed. First, a signal-layer neural network framework was used to fuse the features of individual nodes in the graph, then the aggregation weights of different neighbor entities were changed by adding influence factors. Second, the single-layer was extended to multi-layer by iteration, so that the entities were able to obtain abundant multi-order associated entity information. Finally, the obtained features of entities and users were integrated to generate the prediction score for recommendation. The effects of different aggregation methods and influence factors on the recommendation results were analyzed. Experimental results show that on the datasets MovieLen-1M and Book-Crossing, compared with the benchmark methods such as Factorization Machine Library (LibFM), Deep Factorization Machine (DeepFM), Wide&Deep and RippleNet, KGLN obtains an AUC (Area Under ROC (Receiver Operating Characteristic) curve) improvement of 0.3%-5.9% and 1.1%-8.2%, respectively.

Key words: recommender system, Knowledge Graph (KG), graph neural network, network feature learning, personalized recommendation, interest mining

摘要： 知识图谱（KG）蕴含丰富的结构与关联信息，不仅可以缓解推荐系统中数据稀疏、冷启动等问题，还可以更准确地进行个性化推荐，因此提出一种基于知识图谱驱动的端到端图神经网络推荐模型KGLN。首先使用单层神经网络框架对图中单个节点进行特征融合，并加入影响因子来改变不同邻居实体的聚合权重；然后通过迭代的方式将单层扩展到多层，使实体可以获得丰富的多阶关联实体信息；最后结合实体特征与用户特征产生预测评分进行推荐。分析并研究了不同聚合方法及影响因子对推荐效果的影响。实验结果表明，在数据集MovieLens-1M以及Book-Crossing上与基准方法因子分解库（LibFM）、深度分解机（DeepFM）、Wide&Deep、RippleNet的对比中，KGLN的曲线下面积（AUC）分别提升了0.3%~5.9%和1.1%~8.2%。

CLC Number:

TP183

LIU Huan, LI Xiaoge, HU Likun, HU Feixiong, WANG Penghua. Knowledge graph driven recommendation model of graph neural network[J]. Journal of Computer Applications, 2021, 41(7): 1865-1870.

刘欢, 李晓戈, 胡立坤, 胡飞雄, 王鹏华. 基于知识图谱驱动的图神经网络推荐模型[J]. 计算机应用, 2021, 41(7): 1865-1870.

References

[1] HE X,LIAO L,ZHANG H,et al. Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web. Republic and Canton of Geneva:International World Wide Web Conferences Steering Committee,2017:173-182.
[2] CHENG H T,KOC L,HARMSEN J,et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. New York:ACM,2016:7-10.
[3] HUANG J,ZHAO W X,DOU H,et al. Improving sequential recommendation with knowledge-enhanced memory networks[C]//Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM,2018:505-514.
[4] WANG H,ZHANG F,XIE X,et al. DKN:deep knowledge-aware network for news recommendation[C]//Proceedings of the 2018 World Wide Web Conference. Republic and Canton of Geneva:International World Wide Web Conferences Steering Committee, 2018:1835-1844.
[5] ZHANG F,YUAN N J,LIAN D,et al. Collaborative knowledge base embedding for recommender systems[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2016:353-362.
[6] YU X, REN X, SUN Y, et al. Personalized entity recommendation:a heterogeneous information network approach[C]//Proceedings of the 7th ACM International Conference on Web Search and Data Mining. New York:ACM,2014:283-292.
[7] WANG Q,MAO Z,WANG B,et al. Knowledge graph embedding:a survey of approaches and applications[J]. IEEE Transactions on Knowledge and Data Engineering,2017,29(12):2724-2743.
[8] BORDES, ANTOINE U, NICOLAS G, et al. Translating embeddings for modeling multi-relational data[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems. Red Hook,NY:Curran Associates Inc.,2013:2787-2795.
[9] LIN Y, LIU Z, SUN M, et al. Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence. Palo Alto, CA:AAAI Press,2015:2181-2187.
[10] KIPF T N,WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL].[2020-06-20]. https://arxiv.org/pdf/1609.02907.pdf.
[11] YING R,HE R,CHEN K,et al. Graph convolutional neural networks for web-scale recommender systems[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2018:974-983.
[12] MONTI F,BRONSTEIN M M,BRESSON X. Geometric matrix completion with recurrent multi-graph neural networks[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY:Curran Associates Inc.,2017:3700-3710.
[13] VAN DEN BERG R, KIPF T N, WELLING M. Graph convolutional matrix completion[EB/OL].[2020-06-20]. https://arxiv.org/pdf/1706.02263.pdf.
[14] WU Y,LIU H,YANG Y. Graph convolutional matrix completion for bipartite edge prediction[EB/OL].[2020-06-20]. http://nyc.lti.cs.cmu.edu/yiming/Publications/wu-kdir18.pdf.
[15] GUO H,TANG R,YE Y,et al. DeepFM:a factorizationmachine based neural network for CTR prediction[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. Palo Alto,CA:AAAI Press,2017:1725-1731.
[16] XUE H,DAI X,ZHANG J,et al. Deep matrix factorization models for recommender systems[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. Palo Alto,CA:AAAI Press,2017:3203-3209.
[17] WANG H, ZHANG F, ZHAO M, et al. Multi-task feature learning for knowledge graph enhanced recommendation[C]//Proceedings of the 2019 World Wide Web Conference. New York:ACM,2019:2000-2010.
[18] WANG H,ZHANG F,WANG J,et al. RippleNet:propagating user preferences on the knowledge graph for recommender systems[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. New York:ACM, 2018:417-426.
[19] HAMILTON W L, YING R, LESKOVEC J. Inductive representation learning on large graphs[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook,NY:Curran Associates Inc.,2017:1025-1035.
[20] WANG X,HE X,CAO Y,et al. KGAT:knowledge graph attention network for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2019:950-958.
[21] RENDLE S. Factorization machines with libFM[J]. ACM Transactions on Intelligent Systems and Technology,2012,3(3):No. 57.
[22] WU Z,PAN S,CHEN F,et al. A comprehensive survey on graph neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems,2021,32(1):4-24.

Knowledge graph driven recommendation model of graph neural network

基于知识图谱驱动的图神经网络推荐模型

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	TIAN Ling, ZHANG Jinchuan, ZHANG Jinhao, ZHOU Wangtao, ZHOU Xue. Knowledge graph survey： representation， construction， reasoning and knowledge hypergraph theory [J]. Journal of Computer Applications, 2021, 41(8): 2161-2186.
[2]	ZHANG Rong, ZHANG Xianguo. Opinion spam detection based on hierarchical heterogeneous graph attention network [J]. Journal of Computer Applications, 2021, 41(5): 1275-1281.
[3]	NAN Ning, YANG Chengyi, WU Zhihao. Multi-graph neural network-based session perception recommendation model [J]. Journal of Computer Applications, 2021, 41(2): 330-336.
[4]	SUN Jianqiang, XU Shaohua. Knowledge reasoning method based on differentiable neural computer and Bayesian network [J]. Journal of Computer Applications, 2021, 41(2): 337-342.
[5]	SUN Heli, XU Tong, HE Liang, JIA Xiaolin. Personalized social event recommendation method integrating user historical behaviors and social relationships [J]. Journal of Computer Applications, 2021, 41(2): 324-329.
[6]	DONG Yongfeng, LIU Chao, WANG Liqin, LI Yingshuang. Relationship reasoning method combining multi-hop relationship path information [J]. Journal of Computer Applications, 2021, 41(10): 2799-2805.
[7]	CHEN Xi, MEI Guang, ZHANG Jinjin, XU Weisheng. Student grade prediction method based on knowledge graph and collaborative filtering [J]. Journal of Computer Applications, 2020, 40(2): 595-601.
[8]	DENG Kai, HUNAG Jiajin, QIN Jin. Item-based unified recommendation model [J]. Journal of Computer Applications, 2020, 40(2): 530-534.
[9]	ZHANG Wenlong, QIAN Fulan, CHEN Jie, ZHAO Shu, ZHANG Yanping. Collaborative filtering recommendation algorithm based on dual most relevant attention network [J]. Journal of Computer Applications, 2020, 40(12): 3445-3450.
[10]	HE Haochen, ZHANG Danhong. Recommendation method based on multidimensional social relationship embedded deep graph neural network [J]. Journal of Computer Applications, 2020, 40(10): 2795-2803.
[11]	HAO Zhifeng, KE Yanrong, LI Shuo, CAI Ruichu, WEN Wen, WANG Lijuan. Node classification method in social network based on graph encoder network [J]. Journal of Computer Applications, 2020, 40(1): 188-195.
[12]	TIAN Baojun, YANG Huyun, FANG Jiandong. Recommendation algorithm based on probability matrix factorization and fusing trust [J]. Journal of Computer Applications, 2019, 39(10): 2834-2840.
[13]	SUN Limei, LI Yue, Ejike Ifeanyi Michael, CAO Keyan. Simplified Slope One algorithm for online rating prediction [J]. Journal of Computer Applications, 2018, 38(2): 497-502.
[14]	FENG Yong, HAN Xiaolong, FU Chenping, WANG Rongbing, XU Hongyan. Commodity recommendation method integrating user trust and brand recognition [J]. Journal of Computer Applications, 2018, 38(10): 2886-2891.
[15]	HUANG Kai, ZHANG Xihuang. Singular value decomposition recommender model based on phase sequential effect [J]. Journal of Computer Applications, 2017, 37(5): 1392-1396.