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Determining Similar Recommenders Using Improved Collaborative Filtering in MANETs

机译:使用改进的船只中的协作滤波确定类似的推荐者

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In a MANET environment, recommendation systems face a significant challenge whilst dealing with mobile nodes. Specifically, when a "node" is about to join a new cluster it may require some form of reference from a previously associated cluster. As such, research in this area has primarily focused on the selection of recommender nodes so that the overall consistency of the MANET system is maintained. In this study, an improved collaborative filtering mechanism has been exploited to address the selection of a group of suitable recommenders. First, a cluster formation algorithm has been used to group the set of recommenders based on their similarity measures with predictions computed independently for each cluster. Next, a threshold window is identified for selecting the best group of similar recommenders eliminating the lowest and highest trusted recommenders. Simulation results suggest that the proposed trust based similarity measures can greatly enhance the accuracy of node based trust management scheme.
机译:在漫长的环境中,推荐系统在处理移动节点时面临着重大挑战。具体地,当“节点”即将加入新的群集时,它可能需要先前关联的群集的某种形式的参考。因此,在该区域的研究主要集中在选择推荐节点的选择,以便维护MANET系统的整体一致性。在这项研究中,已经利用改进的协作滤波机制来解决一组合适推荐者的选择。首先,群集形成算法已基于其相似度措施对该组推荐算法进行分组,其中预测为每个群集独立计算。接下来,识别阈值窗口,用于选择最佳的类似推荐员组,消除了最低和最高可信推荐者。仿真结果表明,拟议的基于信任的相似度措施可以大大提高基于节点的信任管理方案的准确性。

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