News recommendation method with knowledge graph and differential privacy

doi:10.11772/j.issn.1001-9081.2021030527

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (5): 1339-1346.DOI: 10.11772/j.issn.1001-9081.2021030527

Special Issue: 人工智能

• Artificial intelligence • Previous Articles Next Articles

News recommendation method with knowledge graph and differential privacy

Li’e WANG¹^,², Xiaocong LI¹, Hongyi LIU²()

^1.School of Computer Science and Engineering，Guangxi Normal University，Guilin Guangxi 541004，China
^2.Guangxi Key Laboratory of Multi?Source Information Mining and Security （Guangxi Normal University），Guilin Guangxi 541004，China

Received:2021-04-08 Revised:2021-07-07 Accepted:2021-07-07 Online:2022-06-11 Published:2022-05-10
Contact: Hongyi LIU
About author:WANG Li’e，born in 1981，M. S.，professor. Her researchinterests include distributed system， network security， privacy protection，recommender system.
LI Xiaocong， born in 1996，M. S. candidate. His research interests include data privacy，recommender system.
LIU Hongyi， born in 1969， M. S.， associate professor. Herresearch interests include data privacy，education information，database.
Supported by:
Guangxi Natural Science Foundation(2020GXNSFAA297075);Systematic Research Project Fund of Guangxi Key Laboratory of Multi-Source Information Mining and Security(19-A-02-02);School-Level Post Graduate Innovation Project of Guangxi Normal University(JXXYYJSCXXM-006)

融合知识图谱和差分隐私的新闻推荐方法

王利娥¹^,², 李小聪¹, 刘红翼²()

^1.广西师范大学计算机科学与工程学院, 广西桂林 541004
^2.广西多源信息挖掘与安全重点实验室(广西师范大学), 广西桂林 541004

通讯作者: 刘红翼
作者简介:王利娥（1981—），女，湖南邵阳人，教授，硕士，CCF会员，主要研究方向：分布式系统、网络安全、隐私保护、推荐系统；
李小聪（1996—），男，湖北黄冈人，硕士研究生，主要研究方向：数据隐私、推荐系统
刘红翼（1969—），女，广西桂林人，副教授，硕士，主要研究方向：数据隐私、教育信息、数据库。 redwing33@sohu.com
基金资助:
广西自然科学基金资助项目(2020GXNSFAA297075);广西多源信息挖掘与安全重点实验室系统性研究课题基金资助项目(19?A?02?02);广西师范大学院级研究生创新项目(JXXYYJSCXXM?006)

Abstract

Abstract:

The existing recommendation method with knowledge graph and privacy protection cannot effectively balance the noise of Differential Privacy （DP） and the performance of recommender system. In order to solve the problem， a News Recommendation method with Knowledge Graph and Privacy protection （KGPNRec） was proposed. Firstly， the multi-channel Knowledge-aware Convolutional Neural Network （KCNN） model was adopted to merge the multi-dimensional feature vectors of news title， entities and entity contexts of knowledge graph to improve the accuracy of recommendation. Secondly， based on the attention mechanism， the noise with different magnitudes was added in the feature vectors according to different sensitivities to reduce the impact of noise on data analysis. Then， the uniform Laplace noise was added to weighted user feature vectors to ensure the security of user data. Finally， the experimental analysis was conducted on real news datasets. Experimental results show that， compared with the baseline methods such as Privacy-Preserving Multi-Task recommendation Framework （PPMTF） and recommendation method based on Deep Knowledge-aware Network （DKN）， the proposed KGPNRec can protect user privacy and ensure the prediction performance of method. For example， on the Bing News dataset， the Area Under Curve （AUC） value， accuracy and F1-score of the proposed method are improved by 0.019， 0.034 and 0.034 respectively compared with those of PPMTF.

Key words: knowledge graph, Differential Privacy (DP), recommender system, news, Convolutional Neural Network (CNN)

摘要：

针对现有融合知识图谱和隐私保护的推荐方法不能有效平衡差分隐私（DP）噪声与推荐系统性能的问题，提出了一种融合知识图谱和隐私保护的新闻推荐方法（KGPNRec）。首先，采用多通道知识感知的卷积神经网络（KCNN）模型融合新闻标题、知识图谱中实体和实体上下文等多维度的特征向量，以提高推荐的准确度；其次，利用注意力机制为不同敏感程度的特征向量添加不同程度的噪声，从而降低噪声对数据分析的影响；然后，对加权的用户特征向量添加统一的拉普拉斯噪声，以保证用户数据的安全性；最后，在真实的新闻数据集上进行实验分析。实验结果表明，与隐私保护的多任务推荐方法（PPMTF）和基于深度知识感知网络（DKN）的推荐方法等相比，所提KGPNRec在保护用户隐私的同时能保证方法的预测性能。在Bing News数据集上，所提方法的曲线下面积（AUC）值、准确率和F1分数与PPMTF相比分别提高了0.019、0.034和0.034。

关键词: 知识图谱, 差分隐私, 推荐系统, 新闻, 卷积神经网络

CLC Number:

TP309

Li’e WANG, Xiaocong LI, Hongyi LIU. News recommendation method with knowledge graph and differential privacy[J]. Journal of Computer Applications, 2022, 42(5): 1339-1346.

王利娥, 李小聪, 刘红翼. 融合知识图谱和差分隐私的新闻推荐方法[J]. 《计算机应用》唯一官方网站, 2022, 42(5): 1339-1346.

Figures/Tables 6

Fig. 1 Framework of KGPNRec

Tab. 1 Statistics of datasets and knowledge graphs

数据集	用户数	新闻数	三元组数	实体数	关系数
Bing News	141 487	535 145	772 258	36 350	1 165
MIND	1 000 000	161 013	332 451	23 584	1 014

Fig. 2 Performance comparison of different methods on Bing News dataset

Fig. 3 Performance comparison of different methods on MIND dataset

Fig. 4 Performance comparison of different methods under different privacy budgets

Tab. 2 Performance comparison among KGPNRec with different variants

模型	F1-score	AUC	Accuracy
KGPNRec	0.668	0.612	0.677
KGPNRec-attention	0.648	0.613	0.657
TransE+KGPNRec	0.659	0.621	0.668
TransH+KGPNRec	0.657	0.628	0.669
TransR+KGPNRec	0.658	0.625	0.660

References 24

1	OKURA S， TAGAMI Y， ONO S， et al. Embedding-based news recommendation for millions of users ［C］// Proceedings of the 2017 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York： ACM， 2017： 1933-1942. 10.1145/3097983.3098108
2	DE SOUZA PEREIRA MOREIRA G. CHAMELEON： a deep learning meta-architecture for news recommender systems ［C］// Proceedings of the 2018 12th ACM Conference on Recommender Systems. New York： ACM， 2018： 578-583. 10.1145/3240323.3240331
3	WU C H， WU F Z， AN M X， et al. NPA： neural news recommendation with personalized attention ［C］// Proceedings of the 2019 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York： ACM， 2019： 2576-2584. 10.1145/3292500.3330665
4	ZHANG L M， LIU P， GULLA J A. Dynamic attention-integrated neural network for session-based news recommendation ［J］. Machine Learning， 2019， 108（10）： 1851-1875. 10.1007/s10994-018-05777-9
5	WANG X J， YU L T， REN K， et al. Dynamic attention deep model for article recommendation by learning human editors’demonstration ［C］// Proceedings of the 2017 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York： ACM， 2017： 2051-2059. 10.1145/3097983.3098096
6	田萱，丁琪，廖子慧，等.基于深度学习的新闻推荐算法研究综述［J］.计算机科学与探索，2021，15（6）：971-998. 10.3778/j.issn.1673-9418.2007021
	TIAN X， DING Q， LIAO Z H， et al. Survey on deep learning based news recommendation algorithm ［J］. Journal of Frontiers of Computer Science and Technology， 2021， 15（6）： 971-998. 10.3778/j.issn.1673-9418.2007021
7	MA W Z， ZHANG M， CAO Y， et al. Jointly learning explainable rules for recommendation with knowledge graph ［C］// Proceedings of the 2019 World Wide Web Conference. New York： ACM， 2019： 1210-1221. 10.1145/3308558.3313607
8	WANG H W， ZHANG F Z， 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. 10.1145/3178876.3186175
9	SHI C， HU B B， ZHAO W X， et al. Heterogeneous information network embedding for recommendation ［J］. IEEE Transactions on Knowledge and Data Engineering， 2019， 31（2）： 357-370. 10.1109/tkde.2018.2833443
10	BOUTET A， FREY D， GUERRAOUI R， et al. Privacy-preserving distributed collaborative filtering ［J］. Computing， 2016， 98（8）：827-846. 10.1007/s00607-015-0451-z
11	YU B， ZHOU C Y， ZHANG C， et al. A privacy-preserving multi-task framework for knowledge graph enhanced recommendation ［J］. IEEE Access， 2020， 8：115717-115727. 10.1109/access.2020.3004250
12	BERLIOZ A， FRIEDMAN A， KAAFAR M A， et al. Applying differential privacy to matrix factorization ［C］// Proceedings of the 2015 9th ACM Conference on Recommender Systems. New York： ACM， 2015： 107-114. 10.1145/2792838.2800173
13	LIU Z Q， WANG Y X， SMOLA A. Fast differentially private matrix factorization ［C］// Proceedings of the 2015 9th ACM Conference on Recommender Systems. New York： ACM， 2015： 171-178. 10.1145/2792838.2800191
14	WANG J， TANG Q. Differentially private neighborhood-based recommender systems ［C］// Proceedings of the 2017 IFIP International Conference on ICT Systems Security and Privacy Protection， IFIPAICT 502. Cham： Springer， 2017： 459-473.
15	RAMAKRISHNAN N， KELLER B J， MIRZA B J， et al. Privacy risks in recommender systems ［J］. IEEE Internet Computing， 2001， 5（6）： 54-63. 10.1109/4236.968832
16	MENG X Y， WANG S H， SHU K， et al. Personalized privacy-preserving social recommendation ［C］// Proceedings of the 2018 32nd AAAI Conference on Artificial Intelligence. Palo Alto： AAAI Press， 2018： 3796-3803.
17	SHOKRI R， STRONATI M， SONG C Z， et al. Membership inference attacks against machine learning models ［C］// Proceedings of the 2017 IEEE Symposium on Security and Privacy. Piscataway： IEEE， 2017： 3-18. 10.1109/sp.2017.41
18	TRAMÈR F， ZHANG F， JUELS A， et al. Stealing machines learning models via prediction APIs ［C］// Proceedings of the 2016 25th USENIX Security Symposium. Berkeley： USENIX Association， 2016： 601-618.
19	DWORK C. A firm foundation for private data analysis ［J］. Communications of the ACM， 2011， 54（1）： 86-95. 10.1145/1866739.1866758
20	JI G L， HE S Z， XU L H， et al. Knowledge graph embedding via dynamic mapping matrix ［C］// Proceedings of the 2015 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing （Volume 1： Long Papers）. Stroudsburg： ACL， 2015： 687-696. 10.3115/v1/p15-1067
21	WANG J， WANG Z Y， ZHANG D W， et al. Combining knowledge with deep convolutional neural networks for short text classification ［C］// Proceedings of the 2017 26th International Joint Conference on Artificial Intelligence. California： IJCAI Organization， 2017： 2915-2921. 10.24963/ijcai.2017/406
22	BORDES A， USUNIER N， GARCIA-DURÁN A， et al. Translating embeddings for modeling multi-relational data ［C］// Proceedings of the 2013 26th International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2013： 2787-2795. 10.1007/978-3-662-44848-9_28
23	WANG Z， ZHANG J W， FENG J L， et al. Knowledge graph embedding by translating on hyperplanes ［C］// Proceedings of the 2014 28th AAAI Conference on Artificial Intelligence. Palo Alto： AAAI Press， 2014： 1112-1119. 10.1609/aaai.v33i01.33017152
24	LIN Y K， LIU Z Y， SUN M S， et al. Learning entity and relation embeddings for knowledge graph completion ［C］// Proceedings of the 2015 29th AAAI Conference on Artificial Intelligence. Palo Alto： AAAI Press， 2015： 2181-2187. 10.1609/aaai.v34i05.6281

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News recommendation method with knowledge graph and differential privacy

融合知识图谱和差分隐私的新闻推荐方法

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