面向Web服务QoS预测的模糊信息熵与区域偏置矩阵分解
Fuzzy Information Entropy and Region Biased Matrix Factorization for Web Service QoS Prediction
AI总结：
针对Web服务QoS预测中现有矩阵分解忽略局部相似性与非交互效应的问题，提出融合模糊信息熵相似邻居识别与区域偏置的矩阵分解方法，在5%-20%矩阵密度下性能优于部分前沿方法。
摘要：
如今互联网上存在大量同类服务，服务质量（Quality of Service, QoS）成为用户关注的核心问题。由于通过用户调用收集所有服务的QoS值并不现实，预测QoS值是更可行的方案。矩阵分解被认为是一种有效的预测方法，但现有大多数矩阵分解算法侧重于捕捉用户与服务之间的全局相似性，忽略了用户与其相似邻居之间的局部相似性，以及用户与服务之间的非交互效应。本文提出一种基于用户信息熵与区域偏置的矩阵分解方法，采用基于模糊信息熵的相似性度量方法识别用户的相似邻居，同时将每个用户与服务之间的区域偏置线性融入矩阵分解，以捕捉用户与服务之间的非交互特征。该方法在更真实复杂的网络环境中表现出更优的预测性能。此外，本文在真实QoS数据集上开展了大量实验，结果表明，在矩阵密度为5%至20%的范围内，所提方法的表现优于该领域部分现有前沿方法。
英文摘要：
Nowadays, there are many similar services available on the internet, making Quality of Service (QoS) a key concern for users. Since collecting QoS values for all services through user invocations is impractical, predicting QoS values is a more feasible approach. Matrix factorization is considered an effective prediction method. However, most existing matrix factorization algorithms focus on capturing global similarities between users and services, overlooking the local similarities between users and their similar neighbors, as well as the non-interactive effects between users and services. This paper proposes a matrix factorization approach based on user information entropy and region bias, which utilizes a similarity measurement method based on fuzzy information entropy to identify similar neighbors of users. Simultaneously, it integrates the region bias between each user and service linearly into matrix factorization to capture the non-interactive features between users and services. This method demonstrates improved predictive performance in more realistic and complex network environments. Additionally, numerous experiments are conducted on real-world QoS datasets. The experimental results show that the proposed method outperforms some of the state-of-the-art methods in the field at matrix densities ranging from 5% to 20%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
