Matrix analysis
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
Opinion-Based Filtering through Trust
CIA '02 Proceedings of the 6th International Workshop on Cooperative Information Agents VI
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
Supporting Trust in Virtual Communities
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 6 - Volume 6
Review on Computational Trust and Reputation Models
Artificial Intelligence Review
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Investigating interactions of trust and interest similarity
Decision Support Systems
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
A model of a trust-based recommendation system on a social network
Autonomous Agents and Multi-Agent Systems
A survey of trust in internet applications
IEEE Communications Surveys & Tutorials
Cross-lingual keyword recommendation using latent topics
Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Transactions on computational science XII
Modelling trust for communicating agents: agent-based and population-based perspectives
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Modeling and Validation of Biased Human Trust
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Vulnerabilities and countermeasures in context-aware social rating services
ACM Transactions on Internet Technology (TOIT)
Identification of influential social networkers
International Journal of Web Based Communities
Building trust communities using social trust
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
Fast track article: Balancing behavioral privacy and information utility in sensory data flows
Pervasive and Mobile Computing
Social network-based recommendation: a graph random walk kernel approach
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Trust assessment: a personalized, distributed, and secure approach
Concurrency and Computation: Practice & Experience
An empirical comparison of social, collaborative filtering, and hybrid recommenders
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
A survey of trust in social networks
ACM Computing Surveys (CSUR)
Users' attachment in trust networks: reputation vs. effort
International Journal of Bio-Inspired Computation
Agent-Based and population-based modeling of trust dynamics
Transactions on Computational Collective Intelligence IX
Design and validation of a relative trust model
Knowledge-Based Systems
ACM Transactions on the Web (TWEB)
Modelling biased human trust dynamics
Web Intelligence and Agent Systems
Hi-index | 0.00 |
We propose a novel trust metric for social networks which is suitable for application to recommender systems. It is personalised and dynamic, and allows to compute the indirect trust between two agents which are not neighbours based on the direct trust between agents that are neighbours. In analogy to some personalised versions of PageRank, this metric makes use of the concept of feedback centrality and overcomes some of the limitations of other trust metrics. In particular, it does not neglect cycles and other patterns characterising social networks, as some other algorithms do. In order to apply the metric to recommender systems, we propose a way to make trust dynamic over time. We show by means of analytical approximations and computer simulations that the metric has the desired properties. Finally, we carry out an empirical validation on a dataset crawled from an Internet community and compare the performance of a recommender system using our metric to one using collaborative filtering.