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| 融合知识图谱和差分隐私的新闻推荐方法 | |
| 引用本文： | 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, 2022, 42(5): 1339-1346. DOI: 10.11772/j.issn.1001-9081.2021030527 |
| 作者姓名： | 王利娥 李小聪 刘红翼 |
| 作者单位： | 广西师范大学 计算机科学与工程学院, 广西 桂林 541004 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 |
| 基金项目： | 广西自然科学基金资助项目（2020GXNSFAA297075）； |
| 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 |
| 收稿时间： | 2021-04-08 |
| 修稿时间： | 2021-07-07 |
News recommendation method with knowledge graph and differential privacy |
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| 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. DOI: 10.11772/j.issn.1001-9081.2021030527 | |
| Authors: | Li’e WANG Xiaocong LI Hongyi LIU |
| Affiliation: | School of Computer Science and Engineering，Guangxi Normal University，Guilin Guangxi 541004，China Guangxi Key Laboratory of Multi?Source Information Mining and Security （Guangxi Normal University），Guilin Guangxi 541004，China |
| 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. |
| Keywords: | knowledge graph Differential Privacy (DP) recommender system news Convolutional Neural Network (CNN) |
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