Power-law distribution aware trust prediction
- Submitting institution
-
University of Greenwich
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 30489
- Type
- E - Conference contribution
- DOI
-
10.24963/ijcai.2018/495
- Title of conference / published proceedings
- Proceedings of the Twenty-Seventh International Joint Conferences on Artificial Intelligence
- First page
- 3564
- Volume
- 0
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- -
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Trust prediction, aiming to predict the trust relations between users in a social network, is a prominent topic in social network analysis. However, one typical property of the trust network is that the trust relations follow a power-law distribution, with most tail users having few trustors. Due to these tail users, the assumption of low rank made by existing methods is seriously violated and becomes unrealistic. In this paper, we propose a simple yet effective method to address this low-rank assumption. The acceptance rate at IJCAI 2018 was 20.5%.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
