Corpus ID: 1777507

SUNNY: A New Algorithm for Trust Inference in Social Networks Using Probabilistic Confidence Models

@inproceedings{Kuter2007SUNNYAN,
  title={SUNNY: A New Algorithm for Trust Inference in Social Networks Using Probabilistic Confidence Models},
  author={U. Kuter and J. Golbeck},
  booktitle={AAAI},
  year={2007}
}
In many computing systems, information is produced and processed by many people. Knowing how much a user trusts a source can be very useful for aggregating, filtering, and ordering of information. Furthermore, if trust is used to support decision making, it is important to have an accurate estimate of trust when it is not directly available, as well as a measure of confidence in that estimate. This paper describes a new approach that gives an explicit probabilistic interpretation for confidence… Expand
Using probabilistic confidence models for trust inference in Web-based social networks
TLDR
SUNNY is described, a new trust inference algorithm that uses probabilistic sampling to separately estimate trust information and the authors' 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. Expand
A New Method of Trust Inference Based on Markov Model for Peer-to-Peer Network
TLDR
A new algorithm is proposed based on the improvements of Markov model and adopts the level factor and confidence to compute the indirect trust inference and computes a more accurate and objective trust inference value. Expand
Rigorous Probabilistic Trust-Inference with Applications to Clustering
TLDR
This work proposes a new trust inference scheme based on the idea that a trust network can be viewed as a random graph, and a chain of trust as a path in that graph, which creates an inferred trust-metric space where the shorter the distance between two people, the higher their trust. Expand
Bayesian based confidence model for trust inference in MANETs
TLDR
A modified Bayesian based confidence model is proposed that gives an explicit probabilistic interpretation of trust for adhoc networks and a trust inference algorithm is described, Modified SUNNY that uses Probabilistic sampling to infer the trust of a node based on the highest confidence estimation. Expand
Computin g Trust Resultant usin g Intervals
An important problem in trust management area is to evaluate the trust value among two nodes in a web of trust using intermediate nodes. This is widely used when the source node has no experience ofExpand
Computing trust resultant using intervals
TLDR
This paper introduces a novel approach for representation of trust and confidence -both together- using intervals and proposes a kind of summation operation that is a method for calculating the resultant of trust opinions and shows that this operator is more accurate for evaluation of trust resultant than the usual method of weighted-averaging. Expand
The Ripple Effect: Change in Trust and Its Impact Over a Social Network
TLDR
This paper presents an experimental study of several types of trust inference algorithms to answer the following questions on trust and change. Expand
A confidence-aware interval-based trust model
TLDR
A novel framework for integrated representation of trust and confidence using intervals, which provides two operations of trust interval multiplication and summation and a time-variant method that considers freshness, expertise level and two similarity measures in confidence estimation is proposed. Expand
Web of Credit: Adaptive Personalized Trust Network Inference From Online Rating Data
TLDR
This paper proposes a new trust model referred to as “Web of credit (WoC),” where one gives credit to those others one has interacted with based on the quality of the information one’s peers have provided, and contributes a WoC-based trust inference algorithm that is adaptive to the change of user profiles by automatically redistributing credit and reinferring trust measures within the network. Expand
Trustworthiness in Networks: A Simulation Approach for Approximating Local Trust and Distrust Values
TLDR
An approach that interprets trust as probability and is able to estimate local trust values on large networks using a Monte Carlo simulation method is proposed and extended to the SimTrust algorithm that incorporates both trust and distrust values. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 10 REFERENCES
Computing and Applying Trust in Web-based Social Networks
TLDR
It is shown that, in the case where the user's opinion is divergent from the average, the trust-based recommended ratings are more accurate than several other common collaborative filtering techniques. Expand
Trust and nuanced profile similarity in online social networks
TLDR
This work isolates several profile features beyond overall similarity that affect how much subjects trust hypothetical users and shows that the profile features discovered in the experiment allow us to more accurately predict trust than when using only overall similarity. Expand
Bayesian network-based trust model
  • Yao Wang, Julita Vassileva
  • Computer Science
  • Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)
  • 2003
TLDR
A Bayesian network-based trust model is presented for a file sharing peer-to-peer application and shows how Bayesian networks provide a flexible method to present differentiated trust and combine different aspects of trust. Expand
Propagating uncertainty in bayesian networks by probabilistic logic sampling
TLDR
Probabilistic logic sampling is a new scheme employing stochastic simulation which can make probabilistic inferences in large, multiply connected networks, with an arbitrary degree of precision controlled by the sample size. Expand
Social Network-based Trust in Prioritized Default Logic
TLDR
This paper provides a coupling between the method for computing trust values in social networks and the prioritized Reiter defaults of (Baader & Hollunder 1995), where specificity of terminological concepts is used to prioritize defaults. Expand
Probabilistic reasoning in intelligent systems - networks of plausible inference
  • J. Pearl
  • Computer Science
  • Morgan Kaufmann series in representation and reasoning
  • 1989
TLDR
The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. Expand
Artificial Intelligence: A Modern Approach
The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications.Expand
Decision-Theoretic Planning: Structural Assumptions and Computational Leverage
TLDR
This paper presents an overview and synthesis of MDP-related methods, showing how they provide a unifying framework for modeling many classes of planning problems studied in AI, and surveys several types of representations for both classical and decision-theoretic planning problems, and planning algorithms that exploit these representations in a number of different ways to ease the computational burden of constructing policies or plans. Expand
Interactive Course-of-Action Planning Using Causal Models
TLDR
A new technique for interactive planning for coalition operations under conditions of uncertainty is described, based on the use of the Air Force Research Laboratory's Causal Analysis Tool (CAT), a system for creating and analyzing causal models similar to Bayesian networks. Expand
Weak, strong, and strong cyclic planning via symbolic model checking
TLDR
This paper formally characterize different planning problems, where solutions have a chance of success, are guaranteed to achieve the goal, or achieve; the goal with iterative trial-and-error strategies ("strong cyclic planning"), and presents planning algorithms for these problem classes. Expand