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A user preference awareness k-neighbour optimised selection algorithm: modelling and implementation

机译：用户偏好感知k邻域优化选择算法：建模与实现

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摘要

With the development of the massive amount of web services, the popular research area is involved with the solution to select the required services according to the personal preference for different users. To solve these urgent problems, a user preference awareness k-neighbour optimised selection algorithm is proposed in this paper. Initially, the potential interest of the user is explored by using association rules method of data mining technology. Then, an expanded LDA model is used to analyse the influence on user preference calculation for different types of services caused from the functional attributes of web services. To improve the accuracy of the service selection result, the ant colony algorithm is modified to optimise the service selection process of k-neighbours in traditional collaborative filtering. The experiment shows that our proposed method leads to a higher accuracy and coverage than the traditional web service selection methods based on the real service set.
机译：随着大量Web服务的发展，解决方案涉及流行的研究领域，以根据不同用户的个人喜好选择所需的服务。针对这些紧急问题，提出了一种用户偏好感知k近邻优化选择算法。最初，通过使用数据挖掘技术的关联规则方法来探索用户的潜在兴趣。然后，使用扩展的LDA模型分析由Web服务的功能属性对不同类型的服务对用户偏好计算的影响。为了提高服务选择结果的准确性，对蚁群算法进行了改进，优化了传统协同过滤中k邻居的服务选择过程。实验表明，与基于真实服务集的传统Web服务选择方法相比，我们提出的方法具有更高的准确性和覆盖范围。

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