Trust evaluation through relationship analysis
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Inferring binary trust relationships in Web-based social networks
ACM Transactions on Internet Technology (TOIT)
Predicting trusts among users of online communities: an epinions case study
Proceedings of the 9th ACM conference on Electronic commerce
Community gravity: measuring bidirectional effects by trust and rating on online social networks
Proceedings of the 18th international conference on World wide web
Trust relationship prediction using online product review data
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Improved trust-aware recommender system using small-worldness of trust networks
Knowledge-Based Systems
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Strength of social influence in trust networks in product review sites
Proceedings of the fourth ACM international conference on Web search and data mining
mTrust: discerning multi-faceted trust in a connected world
Proceedings of the fifth ACM international conference on Web search and data mining
A group trust metric for identifying people of trust in online social networks
Expert Systems with Applications: An International Journal
Classifying Trust/Distrust Relationships in Online Social Networks
SOCIALCOM-PASSAT '12 Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
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Web user trustiness is closely related to web information credibility, which has attracted wide spread concern since the emergence of Web 2.0. Motivated by the randomness and fuzziness of user trust relationships at the web age, this paper proposes a novel user trust relationship prediction method based on cloud model. We start by defining trust cloud, distrust cloud and scoring cloud; then calculate expectation, entropy and hyper-entropy to extract digital characteristics of cloud by utilizing inverse cloud generation algorithm; and finally predict user trust relationships based on trust distance and distrust distance. To analyze the effect of the proposed approach, we conducted experiment on the Extended Epinions dataset, which shows that the precision, recall and F-measure of our method reaches 96.94%, 99.14% and 98.03% respectively, with an increase of 0.21%, 13.11%, and 9.01% over current best method, demonstrating that predicting user trust relations in social networks based on cloud model is reasonable and effective.