情境感知的云服务质量协同预测与个性化推荐研究

云服务质量预测与推荐机制对辅助用户选择所需服务有重要支撑作用，而解决预测机制情境感知能力不足、缺乏有效的个性化服务推荐方法、协同预测数据稀疏敏感等问题是云服务推荐的重要需求。针对这些问题，开展情境感知的云服务质量协同预测与个性化推荐研究，包括：(1)针对泛在环境、稀疏数据条件的云服务质量预测需求，研究基于稀疏线性思想的预测模型，继而采用情境正则化、情境离差化等手段优化模型，实现情境感知预测；(2)为捕获用户个性化的服务需求，建立用户兴趣模型，研究利用隐含主题分析，实现情景感知的用户偏好推理；(3)针对个性化云服务选择的复杂决策需求，基于证据理论研究具有系统性和完整性的推荐决策模型及其近似计算方法；并研发实验系统辅助数据采集和算法测试。项目的实施将建立情境感知的云服务质量预测与推荐方法体系，为泛在环境下的云服务质量预测提供有效的支撑技术，为个性化的云服务选择提供新的解决思路。

QoS prediction and recommendation mechanisms are very important to help users find the desired cloud services. How to resolve the existing fundamental problems, such as a lack of context-awareness in QoS prediction mechanism, a lack of operational approaches for personalized service recommendation, sensitivity of sparseness in collaborative QoS prediction is the imperative demands on recommending cloud services. For these reasons, we examine the fundamental problems of context-aware QoS collaborative prediction and personalized recommendation of cloud services. The main contents of this research proposal are as followings: (1) For the requirements of QoS prediction under the conditions of sparse data and ubiquitous environments, we will study the QoS prediction model based on the sparse linear theory, then optimize the prediction model to achieve contextual-awareness using contextual regularization, deviations-based contextual modeling and other means; (2) To catch personalized requirements of users of cloud services, we will establish the users' interest model, and examine how to infer user preferences using latent topic analysis; (3) For the decision-making needs of personalized cloud service selection, we will study a systematic and complete decision-making model and the corresponding approximate calculation methods on the basis of Dempster-Shafer theory of evidence. In addition, an experimental system will be taken to ensure data collection and algorithmic testing. Implementation of the project will establish the context-aware methodology of QoS prediction and recommendation of cloud services, support the QoS prediction of cloud services under the ubiquitous environment, and provide new ideas for personalized selection of cloud services.