DeciTrustNET · Trust based Decision Support Systems for Social Networks with Uncertain Knowledge
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2017-10-04 → 2020-02-21
- EU contribution
- €183,455
- Participants
- 1
- Scheme
- MSCA-IF-EF-ST
Lines connect the coordinator with its partners.
Results in brief
Trust based Decision Support Systems for Social Networks with Uncertain Knowledge
The world wide success of large scale social information systems such as e-commerce platforms, facilities sharing communities and social networks, make them a very promising paradigm for large scale information sharing and management. However their anonymity and distributed nature may contribute to the propagation of low quality information, attacks and manipulations. In this research proposal we have created a novel trust and reputation based framework for social choice in such networks. The proposed system takes into consideration the users’ relationships, the historic evolution of their reputations and their profile similarity to develop a tamper resilient network that guarantees trustworthy communications and transactions. This model paves the way for an entirely new approach of exploiting the massive information stored in social networks to develop an automated trust based decision-support system under uncertainty and incomplete information. The proposed application has great outreach/commercialisation potential in both e-health environments and marketing and recommender systems. Objectives: •Establish a new SNA framework for managing multiple inconsistent heterogeneous information sources that enables the implementation of trust. •To define trust propagation and aggregation operators for trust networks. •To create a trust based feedback mechanism to provide personalised recommendations •To develop a mobile e-health platform based on the proposed trust based social network.
Data: CORDIS, © European Union
Project objective
In real world decision-making, such as public security, social choice or recommender systems, we have a large body of data from various networked heterogeneous information sources or individuals that often conflict with each other and provide inconsistent knowledge. It is a challenging task to yield an optimal consensus decision, given the range of individual decisions obtained in terms of these knowledge sources. This research proposal aims to create a novel mathematical and computational framework for trust based social choice in networks and with uncertain knowledge by merging multiple individuals’ preferences in an adaptive manner to reduce the disagreements among them, and automatically seek a decision or provide a recommendation with a maximal consensus. To achieve our goal, we propose to bring together, for the first time, four previously disparate strands of research: social network analysis, fuzzy preference modelling, multiple attribute group decision-making and game theoretic modelling of malicious users. As a showcase the proposed framework will be incorporated to an e-health recommender platform to increase healthy lifestyle in cancer survivors. Both the fellow and the host researcher have extensive research experience in decision support system under uncertainty, e-health platforms and software and mobile development. With the foundational theory already in place, and given the growing interest in decision support systems and social networks, the time is right for pursuing this research. Being executable, this model will pave the way for an entirely new form of automated decision-support under uncertainty and incomplete information in dynamic environments and, at the same time will greatly expand the fellow knowledge and skill set, and help her to develop into a leading independent researcher. The proposed application has great outreach/commercialisation potential and contributes towards the H2020 Health, Demographic Change and Wellbeing challenge.
Original text from CORDIS.
Participants
- DE MONTFORT UNIVERSITY · LeicesterCoordinatorUnited Kingdom
Links
Data: CORDIS, © European Union