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

机译:用于无线网状网络中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:过去十年来,在互联网上的大量用户与他人共享有价值的信息的意义上,有一种范式的技术转变。在此字段中工作的用户在不同的级别工作以获取信息共享。由于这些用户彼此共享信息,因此需要有效的协作机制,以实现每个级别的效率和准确性。因此,为了实现高水平的效率和准确性,本文提出了一种用于服务选择的分布式上下文感知协作滤波(CF)方法。用户配置文件是从不同用户的先前数据的数据库存储库创建为数据库存储库及其各自的利益。对于想要利用特定服务的新用户,系统将请求与现有用户配置文件匹配,如果找到匹配项,则基于他的个人资料向他推荐合适的服务。为了选择与现有用户匹配其配置文件的用户选择的相关内容,包括基于用户输入的分布式滤波度量(DFM)。此外,现有用户简档及其兴趣的交叉点也包括在这种度量中,以具有高级别的精度。具体而言,我们已经将电影选择的示例作为某些网络向用户提供的服务。所选择的底层网络是由于诸如低部署成本等诸如低部署成本和维护等特殊功能而被近年来成为一种新的强大技术的无线网状网络(WMN)。提出了一种新颖的上下文感知服务选择(CASS)算法。基于效率和准确性评估所提出的算法的性能。得到的结果表明,该方法具有高效率和准确性。

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