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Improved web service recommendation via exploiting location and QoS information

机译：通过利用位置和QoS信息改进Web服务推荐

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Web services describe a way of integrating web-based applications that help in machine-to-machine interaction over the network. There are many publicly available web services and the number keeps on increasing. However this ever increasing pool makes it difficult for optimal service selection. So, appropriate selection of web service suiting the requirements of user is a non-trivial task. Our research proposes technique which helps in optimal service selection with optimal Quality-of-Service (QoS) performance. The technique designs a recommender system based on Collaborative Filtering (CF) algorithm which employs location and QoS values to cluster users and services. The main objective of the proposed technique is to address the issue of data sparsity and scalability. The proposed approach uses k-Nearest Neighbor (kNN) algorithm with Support Vector Machine (SVM) in CF algorithm framework. SVM a state-of-the-art classification algorithm used to address the issue of sparse data and k-NN used with CF algorithm for similarity mapping of user and services.
机译：Web服务描述了一种集成基于Web的应用程序，这些应用程序有助于通过网络计算机到机器交互。有许多可公开的Web服务，数字继续增加。然而，这是一个越来越多的池使得最佳的服务选择很难。 So, appropriate selection of web service suiting the requirements of user is a non-trivial task.我们的研究提出了有助于最佳的服务选择，具有最佳的服务质量（QoS）性能。该技术设计基于协同滤波（CF）算法的推荐系统，该算法将位置和QoS值用于群集用户和服务。拟议技术的主要目标是解决数据稀疏性和可扩展性问题。所提出的方法在CF算法框架中使用带有支持向量机（SVM）的K-COMBERY邻（KNN）算法。 SVM一种最先进的分类算法，用于解决与CF算法用于用户和服务的相似性映射的CF算法的稀疏数据和K-NN的问题。

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