A group trust metric for identifying people of trust in online social networks

Aloufi, Samah; Kim, Heung-Nam; Saddik, Abdulmotaleb El

doi:10.1016/j.eswa.2012.05.084

Cited by 76 publications

(31 citation statements)

References 18 publications

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“…In addition, Advogato (Al-Oufi et al, 2012) attempts to determine the set of trusted users as many as possible while reducing the influence of unreliable attackers. Advogato utilizes a social graph that represents Advogato's members and their relationships.…”

Section: Related Workmentioning

confidence: 99%

Wright–Fisher multi-strategy trust evolution model with white noise for Internetware

Yin¹,

Wang²,

Dong³

et al. 2013

Expert Systems with Applications

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Section: Related Workmentioning

confidence: 99%

Wright–Fisher multi-strategy trust evolution model with white noise for Internetware

Yin¹,

Wang²,

Dong³

et al. 2013

Expert Systems with Applications

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“…Reference [8][9][10] discussed trust prediction method using comment data. Samah Al-Oufi et al proposed Extended Advogato Trust Metric Method [11], which builds social graphs from membership in a social network, assigns capacity to the social graph based on some given trust relation and solve the trust reasoning problem by transforming it into a maximum flow problem. This method establishes a complete model, but suffers from high complexity in the solution to the maximum problem itself.…”

Section: Introductionmentioning

confidence: 99%

Web User Trust Relationship Prediction based on Evidence Theory

Bao

Yao

Zuo

et al. 2013

2013 International Conference on Computer Sciences and Applications

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Mining web user trust relationship is important in web information credibility analysis. Motivated by the imprecise nature of trustiness, we propose a novel web user trust prediction method based on evidence theory, which uses user ratings to infer trust relationships between users, where each rating score is treated as an evidence. The method first generates basic probability assignments with training sample set using classical statistics; then, integrates conflict resolution rules and D-S evidence combination rules to obtain global BPA; finally, identifies user trust relationships based on global BPA. Our experiments conducted on the Extended Epinions dataset show that the precision, recall and F-measure of the proposed approach reaches 96.78%, 99.44% and 98.10% respectively, with 0.06%, 13.41% and 9.09% improvement over current best practices, demonstrating the advantages and effectiveness of using evidence theory to identify user trust relationship in social networks.

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“…The early pioneer recommender system, MovieLens, publicly shared the data set has been used in a number of recommendations-related studies and still does. The popularity of movies as the target items of personalized recommendations has been further increased by the Netflix Prize 4 [111]. Since some of these systems used trust links and some use friendship links, thus both of these link types have been explored as a source of data to recommend movies.…”

Section: Applications and Target Items Of Social Link-based Recommendmentioning

confidence: 99%

Recommendations Based on Social Links

Lee

Brusilovsky

2018

Social Information Access

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Abstract. The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues and the future direction. Among several branches of social recommendations, specifically, this chapter focuses on recommendations, which are founded on users' self-defined (i.e., explicit) social links and target to suggest desirable items, not social links of interests. This chapter provides a brief overview over the technical needs for social link-based recommendations and the studies explaining the viability of users' social networks as useful information sources from a social science point of view. Then, the in-depth understanding of existing recommendations based on users' social links will be addressed. Lastly, various issues and future directions of social link-based recommendation research are put forward. IntroductionThe remarkable popularity of social media encourages web users to participate in various online activities and to generate data on an unprecedented scale. Given exponentially growing social media data volumes, personalized recommendation technologies have proven the effectiveness as a solution of the information overload problem; have offered positive user experiences with more relevant contents; and have presented competitive advantage to the social media business [119]. As predictive analysis of online users' tastes, recommendation technology adaptively filters out unnecessary data and selectively chooses presumably favorable information according to users' preferences. At odds with the original principle of personalized recommendations, which is to give users control of information access, however, the typical and most popular collaborative filtering recommendation technology performs all steps autonomously and allows no room for users to get involved in information personalization of their own. The lacking user involvement causes several problems (illustrated in Section 12.3.1), and many researchers have called attention to solutions to improve the quality of typical recommendation technology and to cope with the relevant problems. Among the several streams of research to develop the solutions, one important evolution is to take advantage of users' self-defined online social networks and proposes to generate recommendations based on users' social linkssocial link-based recommendations. The idea to utilize users' online social links as a foundation of recommendation is undoubtedly spurred by the recent phenomenon of online social networks.Compared with the era when web users stayed in isolation, a number of social media systems have been adapting online sociability, helping users to find people of interest, and as well as encouraging them to socially associate with people of interest. In fact, web users' active and eager participation on social media are intrinsically motivated by not only personal desire for information management and knowledge acquisition but also social desire for engagement and communication with...

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A group trust metric for identifying people of trust in online social networks

Cited by 76 publications

References 18 publications

Wright–Fisher multi-strategy trust evolution model with white noise for Internetware

Wright–Fisher multi-strategy trust evolution model with white noise for Internetware

Web User Trust Relationship Prediction based on Evidence Theory

Recommendations Based on Social Links

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