用信誉与深度学习结合，提升网页服务质量预测精度
RAHN: A Reputation Based Hourglass Network for Web Service QoS Prediction
- 引入用户与服务信誉模块，融合聚类与逻辑回归计算信誉值
- 通过多尺度注意力机制聚合特征，降低平均绝对误差和均方根误差
- 适合需要高精度服务质量推荐的系统开发者参考
随着网页服务同质化日益严重，服务推荐难度不断上升。如何更高效、准确地预测服务质量（QoS）成为关键挑战。针对信誉与深度学习在QoS预测中的优势，本文提出一种基于信誉与深度学习的预测网络RAHN，包含信誉计算模块（RCM）、隐式特征提取模块（LFEM）和QoS预测沙漏网络（QPHN）。RCM利用聚类算法和逻辑回归模型分别计算用户与服务信誉；LFEM从已有信息中提取隐式特征，生成初始特征向量；QPHN通过注意力机制聚合多尺度特征向量，并可堆叠多次以获得最终预测特征向量。在真实QoS数据集上的实验表明，与六种基线方法相比，RAHN在平均绝对误差（MAE）和均方根误差（RMSE）上均表现更优。
原文摘要 · Abstract (English)
As the homogenization of Web services becomes more and more common, the difficulty of service recommendation is gradually increasing. How to predict Quality of Service (QoS) more efficiently and accurately becomes an important challenge for service recommendation. Considering the excellent role of reputation and deep learning (DL) techniques in the field of QoS prediction, we propose a reputation and DL based QoS prediction network, RAHN, which contains the Reputation Calculation Module (RCM), the Latent Feature Extraction Module (LFEM), and the QoS Prediction Hourglass Network (QPHN). RCM obtains the user reputation and the service reputation by using a clustering algorithm and a Logit model. LFEM extracts latent features from known information to form an initial latent feature vector. QPHN aggregates latent feature vectors with different scales by using Attention Mechanism, and can be stacked multiple times to obtain the final latent feature vector for prediction. We evaluate RAHN on a real QoS dataset. The experimental results show that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of RAHN are smaller than the six baseline methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。