基于模糊熵与区域偏置的推荐模型，提升服务质量预测精度
Fuzzy Information Entropy and Region Biased Matrix Factorization for Web Service QoS Prediction
- 用模糊信息熵识别用户相似邻居，捕捉局部相似性
- 引入用户-服务区域偏置项，建模非交互特征，提升预测效果
- 在5%~20%数据密度下优于主流方法，适合真实复杂网络场景
当前互联网中存在大量相似服务，服务质量（QoS）成为用户关注重点。由于通过用户调用收集所有服务的QoS值不现实，预测成为更可行方案。矩阵分解被广泛认为是有效预测方法，但现有算法多关注用户与服务间的全局相似性，忽视了用户与其相似邻居的局部相似性，以及用户与服务间的非交互影响。本文提出一种基于用户信息熵和区域偏置的矩阵分解方法，利用模糊信息熵构建相似性度量以识别用户相似邻居，并将每个用户与服务间的区域偏置线性融入矩阵分解，以捕捉非交互特征。该方法在更真实复杂的网络环境中表现出更强的预测性能。在真实QoS数据集上进行了大量实验，结果表明，在矩阵密度为5%至20%时，该方法优于部分当前最优方法。
原文摘要 · Abstract (English)
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%.
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