Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
Bayesian Network-Based Trust Model
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Attack-resistant trust metrics for public key certification
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
SUNNY: a new algorithm for trust inference in social networks using probabilistic confidence models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Recursive noisy OR - a rule for estimating complex probabilistic interactions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Case Study of Collaboration and Reputation in Social Web Search
ACM Transactions on Intelligent Systems and Technology (TIST)
Vulnerabilities and countermeasures in context-aware social rating services
ACM Transactions on Internet Technology (TOIT)
Generalized framework for personalized recommendations in agent networks
Autonomous Agents and Multi-Agent Systems
Clustering users to explain recommender systems' performance fluctuation
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
A multi-dimensional and event-based model for trust computation in the social web
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
Modeling Decentralized Reputation-Based Trust for Initial Transactions in Digital Environments
ACM Transactions on Internet Technology (TOIT)
Bayesian Inference in Trust Networks
ACM Transactions on Management Information Systems (TMIS)
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In this article, we describe a new approach that gives an explicit probabilistic interpretation for social networks. In particular, we focus on the observation that many existing Web-based trust-inference algorithms conflate the notions of “trust” and “confidence,” and treat the amalgamation of the two concepts to compute the trust value associated with a social relationship. Unfortunately, the result of such an algorithm that merges trust and confidence is not a trust value, but rather a new variable in the inference process. Thus, it is hard to evaluate the outputs of such an algorithm in the context of trust inference. This article first describes a formal probabilistic network model for social networks that allows us to address that issue. Then we describe SUNNY, a new trust inference algorithm that uses probabilistic sampling to separately estimate trust information and our confidence in the trust estimate and use the two values in order to compute an estimate of trust based on only those information sources with the highest confidence estimates. We present an experimental evaluation of SUNNY. In our experiments, SUNNY produced more accurate trust estimates than the well-known trust inference algorithm TidalTrust, demonstrating its effectiveness. Finally, we discuss the implications these results will have on systems designed for personalizing content and making recommendations.