As web-based social network allows anyone to post the content without any restriction, the trustworthiness of the content creator plays an important role before using the content. An effiective way to find the trustworthiness is, by analyzing the web resources related to the content creator. Therefore the trustworthiness is assessed using the provenance based ontological model called W7 model. Since it is a real time data, the computed trust for each reviewer using the ontological model is uncertain and vague. An appropriate way to classify such data is using the fuzzy logic with gradual trust level. As the computed trust data are feature-based and non-symbolic, the classification ambiguity need to be reduced greatly. This is achieved with the fuzzy decision tree approach, which is a fusion of fuzzy sets with decision tree. The truth of the rule is crucial in trustworthy user classification, as highly truthful rules really increase the credibility of the user in their domain. Therefore, in the proposed model, degree of truth is used as a pruning criteria that classifies the users with minimum number of fuzzy evidence or knowledge. This paper proposes a semantic provenance based gradual trust model to classify the trustworthy reviewers in a book-based social networks using fuzzy decision tree approach. Performance analysis of the proposed model in the terms of classifier accuracy, precision, recall, the number of rules generated and its time complexity are discussed. The analysis shows that the proposed learning model outperforms other classification models. This method is also applied to other data sets and the performance of the classifier is assessed.
An online social network (OSN) is crowded with people and their huge number of post and hence filtering truthful content and/or filtering truthful content creator is a great challenge. The online recommender system helps to get such information from OSN and suggest the valuable item or user. But in reality people have more belief on recommendation from the people they trust than from untrusted sources. Getting recommendation from the trusted people derived from social network is called Trust-Enhanced Recommender System (TERS). A Trust-Boosted Recommender System (TBRS) is proposed in this paper to address the challenge in identifying trusted users from social network. The proposed recommender system is a fuzzy multi attribute recommender system using boosted vector similarity measure designed to predict trusted users from social networks with reduced error. Performance analysis of the proposed model in terms of accuracy measures such as precision@k and recall@k and error measures, namely, MAE, MSE and RMSE is discussed in this paper. The evaluation shows that the proposed system outperforms other recommender system with minimum MAE and RMSE.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.