基于特征深度融合的Web服务QoS联合预测

摘要/Abstract

摘要： 为了解决 Web服务 QoS预测准确度不够的问题，针对 QoS中隐藏的环境偏好信息和多类 QoS隐藏的共同特征，提出一种基于深度融合的 Web服务 QoS联合预测方法。考虑 QoS数据可以建模为用户 -服务二部图，采用多组件图卷积神经网络进行特征提取和映射，采用加权融合方法对多类 QoS特征进行同维映射。使用注意力因子分解机对映射后的特征向量进行一阶特征、二阶交互特征和高阶交互特征的提取，并结合各部分结果实现 QoS联合预测。实验结果表明，所提出的方法在均方根误差和平均绝对误差方面优于现有 QoS预测方法。

关键词: 联合预测, 服务质量 , 偏好特征深度融合

Abstract: In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features is proposed with considering of the hidden environmental preference information in QoS and the common features of multi-class QoS. First, QoS data is modeled as a user-service bipartite graph; then, multi-component graph convolution neural network is used for feature extraction and mapping, and the weighted fusion method is used for the same dimensional mapping of multi-class of QoS features. Subsequently, the attention factor decomposition machine is used to extract the first-order features, second-order interactive features, and high-order interactive features of the mapped feature vector. Finally, the results of each part are combined to achieve the joint QoS prediction. The experimental results show that the proposed method is superior to the existing QoS prediction methods in terms of root mean square error (RMSE) and average absolute error (MAE).

Key words: Joint Prediction, Quality of Service, Preference Feature, Deep Fusion

. 基于特征深度融合的Web服务QoS联合预测[J]. 通信学报.

参考文献

[1] 方晨 , 张恒巍 , 张铭 , et al. 基于信任扩展和列表级排序学习的服务
推荐方法 [J]. 通信学报 , 2018, 39(01): 147-58.
[2] 赵晨阳 , 王俊岭 . 基于隐含上下文支持向量机的服务推荐方法 [J]. 通信学报 , 2019, 40(09): 61-73.
[3] YIN Y, CHEN L, XU Y, et al. QoS prediction for service recommendation with deep feature learning in edge computing environment [J]. Mobile Networks and Applications, 2019: 1-11.
[4] WANG X, WANG R, SHI C, et al. Multi-component graph convolutional collaborative filtering; proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, F, 2020 [C].
[5] DING L, KANG G, LIU J, et al. QoS Prediction for Web Services via Combining Multi-component Graph Convolutional Collaborative Filtering and Deep Factorization Machine; proceedings of the 2021 IEEE International Conference on Web Services (ICWS), F, 2021 [C]. IEEE.
[6] SHAO L, ZHANG J, WEI Y, et al. Personalized qos prediction forweb services via collaborative filtering; proceedings of the Ieee international conference on web services (icws 2007), F, 2007 [C]. IEEE.
[7] CHEN L, FENG Y, WU J, et al. An enhanced QoS prediction approach for service selection; proceedings of the 2011 IEEE International Conference on Services Computing, F, 2011 [C]. IEEE.
[8] 任丽芳 , 王文剑 . 一种移动边缘计算环境中服务 QoS的预测方法
[J]. 小型微型计算机系统 , 2020, 41(06): 1176-81.
[9] WANG S, ZHAO Y, HUANG L, et al. QoS prediction for service recommendations in mobile edge computing [J]. Journal of Parallel and Distributed Computing, 2019, 127: 134-44.
[10] 邓璇 , 吕晟凯 . 基于信誉感知与嵌入式学习的 Web服务 QoS预测研
究 [J]. 物联网技术 , 2021, 11(12): 99-100+3.
[11] SALAKHUTDINOV R, MNIH A, HINTON G. Restricted Boltzmann machines for collaborative filtering; proceedings of the Proceedings of the 24th international conference on Machine learning, F, 2007 [C].
[12] LUO X, ZHOU M, WANG Z, et al. An effective scheme for QoS estimation via alternating direction method-based matrix factorization [J]. IEEE Transactions on Services Computing, 2016, 12(4): 503-18.
[13] 鲁城华 , 寇纪淞 . 基于用户和服务区域信息的个性化 Web服务质量
预测 [J]. 管理科学 , 2020, 33(02): 63-75.
[14] CHEN L, XIE F, ZHENG Z, et al. Predicting quality of service via leveraging location information [J]. Complexity, 2019, 2019.
[15] TANG M, LIANG W, YANG Y, et al. A factorization machine-based QoS prediction approach for mobile service selection [J]. IEEE Access, 2019, 7: 32961-70.
[16] 夏会 , 高旻 , 邹淑 . 时空感知下基于结构相似度的 Web服务质量预
测 [J]. 重庆大学学报 , 2021, 44(01): 88-96.
[17] 陈蕾 , 杨庚 , 陈正宇 , et al. 基于结构化噪声矩阵补全的 Web服务 QoS预测 [J]. 通信学报 , 2015, 36(06): 53-63.
[18] LUO X, WU H, YUAN H, et al. Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors [J]. IEEE Trans Cybern, 2020, 50(5): 1798-809.
[19] KANG G, LIU J, XIAO Y, et al. Neural and Attentional Factorization Machine-Based Web API Recommendation for Mashup Development [J]. IEEE Transactions on Network and Service Management, 2021, 18(4): 4183-96.
[20] GAO H, XU Y, YIN Y, et al. Context-aware QoS prediction with neural collaborative filtering for Internet-of-Things services [J]. IEEE Internet of Things Journal, 2019, 7(5): 4532-42.
[21] 王安迪 . 基于 QoS的 Web服务质量预测方法研究 [D]; 华北电力大
学(北京 ), 2020.
[22] CHEN D, GAO M, LIU A, et al. A Recurrent Neural Network Based Approach for Web Service QoS Prediction; proceedings of the 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD), F, 2019 [C]. IEEE.
[23] AK E, CANBERK B. Forecasting Quality of Service for Next-Generation Data-Driven WiFi6 Campus Networks [J]. IEEE Transactions on Network and Service Management, 2021.
[24] ZHANG X, LI L. Attentional Neural Factorization Machines for Knowledge Tracing [M]. Knowledge Science, Engineering and Management. 2021: 319-30.
[25] RENDLE S. Factorization machines with libfm [J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2012, 3(3): 1-22.
[26] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [J]. Advances in neural information processing systems, 2017, 30.
[27] ZHENG Z, MA H, LYU M R, et al. QoS-aware web service recommendation by collaborative filtering [J]. IEEE Transactions on services computing, 2010, 4(2): 140-52.
[28] ZHENG Z, MA H, LYU M R, et al. Collaborative web service qos prediction via neighborhood integrated matrix factorization [J]. IEEE Transactions on Services Computing, 2012, 6(3): 289-99.
[29] WU H, ZHANG Z, LUO J, et al. Multiple attributes QoS prediction via deep neural model with contexts [J]. IEEE Transactions on Services Computing, 2018, 14(4): 1084-96.
[30] LOUIZOS C, WELLING M, KINGMA D P. Learning sparse neural networks through L0 regularization [J]. arXiv preprint arXiv:171201312, 2017.