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Distributed Context Aware Collaborative Filtering Approach for Service Selection in Wireless Mesh Networks

机译:无线网状网络中用于服务选择的分布式上下文感知协同过滤方法

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In last decade, there is a paradigm shift in technology in the sense that large numbers of users over the internet share the valuable information with others. Users working in this field work at different levels for information sharing. As these users share the information with each other, there is a need of efficient collaborative mechanism among them to achieve efficiency and accuracy at each level. So to achieve high level of efficiency and accuracy, a distributed context aware collaborative filtering (CF) approach for service selection is proposed in this paper. Users profiles are created as a database repository from the previous data of different users and their respective interests. For the new user who wants to avail a particular service, system matches the request with the existing users profiles and if the match is found then a suitable service is recommended to him based upon his profile. To select the relevant contents of user choice that match his profile with the existing users, a Distributed Filtering Metric (DFM) is included which is based upon user input. Moreover, the intersection of existing users profiles and their interests is also included in this metric to have high level of accuracy. Specifically, we have taken an example of movie selection as a service offered to the users by some network. The underlying network chosen is Wireless Mesh Networks (WMNs) which are emerged as a new powerful technology in recent years due to the unique features such as low deployment cost and easy maintenance. A novel Context Aware Service Selection (CASS) algorithm is proposed. The performance of the proposed algorithm is evaluated with respect to efficiency and accuracy. The results obtained show that the proposed approach has high level of efficiency and accuracy.
机译:在过去的十年中,从互联网上的大量用户与他人共享有价值的信息的角度来看,技术发生了范式转变。在该领域工作的用户可以在不同级别上进行信息共享。由于这些用户彼此共享信息,因此需要一种有效的协作机制来实现每个级别的效率和准确性。因此,为了达到较高的效率和准确性,本文提出了一种分布式的上下文感知协同过滤(CF)方法进行服务选择。根据不同用户的先前数据及其各自的兴趣,将用户概要文件创建为数据库存储库。对于想要使用特定服务的新用户,系统将请求与现有用户配置文件进行匹配,如果找到匹配项,则根据他的配置文件向他推荐合适的服务。为了选择与他的个人资料与现有用户相匹配的用户选择的相关内容,包括了基于用户输入的分布式筛选指标(DFM)。此外,现有用户配置文件及其兴趣的交集也包含在此度量中,以具有较高的准确性。具体而言,我们以电影选择作为示例由某些网络提供给用户的服务为例。选择的基础网络是无线网状网络(WMN),由于其独特的功能(如低部署成本和易于维护),近年来已成为一种强大的新技术。提出了一种新颖的上下文感知服务选择算法。相对于效率和准确性,评估了所提出算法的性能。结果表明,该方法具有较高的效率和准确性。

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