A novel community-based trust aware recommender systems for big data cloud service networks

Abstract

Internet of Things (IoT) has enhanced the appearance of the information and communications technology (ICT) infrastructure of smart sustainable cities that emerge as a growth of the urban development. The potential growth of environment sustainabilities is coming into the existence of the insightful visions of ICT that include big data analytics as a computing prototype. It may draw a penetrative path to mitigate the environmental effects that utilize the natural resource to manage the intelligent infrastructure and services. Moreover, the sustainable development process plays a crucial role to catalyze IoT and big-data application that connects the economic growth with the quality of smart cities. The IoT-enabled big-data applications choose a suitable thematic analysis to direct the use of recommendation systems. It supports users in the irresistible task of examination through large quantities of data in order to select appropriate information or items. Traditional recommender systems rely on similar neighbors irrespective of their preferences/choices when computing the prediction and assuming the users to be autonomous and indistinguishable to disregard the social events between the social users which are highly not reliable. Based on this intuition in this framework, the architecture of community-based trust aware recommender systems (CB-TARS) is proposed. Users’ preferences are expressed by incorporating trusted neighbors within the community of the target user are merged in order to find similar preferences. In addition, the worth of merged ratings is measured by the confidence considering the number of ratings inside the community and the percentage of clashes between negative and positive views. Further, rating confidence is incorporated into the computation of user similarity. The prediction for an unrated item is computed by aggregating the ratings of similar users within the community. Experimental results on real-world data set validate that the proposed method surpasses other complements in terms of accuracy.