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Controversial users demand local trust metrics: an experimental study on Epinions.com community

Published: 09 July 2005 Publication History

Abstract

In today's connected world it is possible and very common to interact with unknown people, whose reliability is unknown. Trust Metrics are a recently proposed technique for answering questions such as "Should I trust this user?". However, most of the current research assumes that every user has a global quality score and that the goal of the technique is just to predict this correct value. We show, on data from a real and large user community, Epinions.com, that such an assumption is not realistic because there is a significant portion of what we call controversial users, users who are trusted and distrusted by many. A global agreement about the trustworthiness value of these users cannot exist. We argue, using computational experiments, that the existence of controversial users (a normal phenomena in societies) demands Local Trust Metrics, techniques able to predict the trustworthiness of an user in a personalized way, depending on the very personal view of the judging user.

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cover image Guide Proceedings
AAAI'05: Proceedings of the 20th national conference on Artificial intelligence - Volume 1
July 2005
481 pages
ISBN:157735236x

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  • AAAI: American Association for Artificial Intelligence

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AAAI Press

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Published: 09 July 2005

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  • (2020)Matrix completion with hierarchical graph side informationProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3496484(9061-9074)Online publication date: 6-Dec-2020
  • (2019)Towards a model of interpersonal trust in Social Media ApplicationsProceedings of the 5th EAI International Conference on Smart Objects and Technologies for Social Good10.1145/3342428.3342674(120-123)Online publication date: 25-Sep-2019
  • (2019)Majority vote and monopolies in social networksProceedings of the 20th International Conference on Distributed Computing and Networking10.1145/3288599.3288633(342-351)Online publication date: 4-Jan-2019
  • (2019)NeuralWalk: Trust Assessment in Online Social Networks with Neural NetworksIEEE INFOCOM 2019 - IEEE Conference on Computer Communications10.1109/INFOCOM.2019.8737469(1999-2007)Online publication date: 29-Apr-2019
  • (2018)Binary rating estimation with graph side informationProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3327144.3327340(4277-4288)Online publication date: 3-Dec-2018
  • (2018)Incremental DFS algorithmsProceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms10.5555/3174304.3175270(53-72)Online publication date: 7-Jan-2018
  • (2018)Inferring and calculating trust for trust-based recommendationsProceedings of the 22nd Pan-Hellenic Conference on Informatics10.1145/3291533.3291572(10-15)Online publication date: 29-Nov-2018
  • (2017)Modelling trust networks using resistive circuits for trust-aware recommender systemsJournal of Information Science10.1177/016555151662873343:1(135-144)Online publication date: 1-Feb-2017
  • (2017)Predicting Trust Relations Within a Social NetworkProceedings of the 2017 ACM on Web Science Conference10.1145/3091478.3091494(53-62)Online publication date: 25-Jun-2017
  • (2017)Emotional and Linguistic Cues of Depression from Social MediaProceedings of the 2017 International Conference on Digital Health10.1145/3079452.3079465(127-136)Online publication date: 2-Jul-2017
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