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k-Level Truthful Incentivizing Mechanism and Generalized k-MAB Problem.
- Source :
-
IEEE Transactions on Computers . Jul2022, Vol. 71 Issue 7, p1724-1739. 16p. - Publication Year :
- 2022
-
Abstract
- Multi-armed bandits problem has been widely utilized in economy-related areas. Incentives are explored in the sharing economy to inspire users for better resource allocation. Previous works build a budget-feasible incentive mechanism to learn users’ cost distribution. However, they only consider a special case that all tasks are considered as the same. The general problem asks for finding a solution when the cost for different tasks varies. In this paper, we investigate this problem by considering a system with $k$ k levels of difficulty. We present two incentivizing strategies for offline and online implementation, and formally derive the ratio of utility between them in different scenarios. We propose a regret-minimizing mechanism to decide incentives by dynamically adjusting budget assignment and learning from users’ cost distributions. We further extend the problem to a more generalized k-MAB problem by removing the contextual information of difficulties. CUE-UCB algorithm is proposed to address the online advertisement problem for multi-platforms. Our experiment demonstrates utility improvement about 7 times and time saving of 54% to meet a utility objective compared to the previous works in sharing economy, and up to 175% increment of utility for online advertising. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189340
- Volume :
- 71
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Computers
- Publication Type :
- Academic Journal
- Accession number :
- 157325223
- Full Text :
- https://doi.org/10.1109/TC.2021.3105831