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Award ID contains: 1850045

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  1. ; ; ; ; (, IEEE Transactions on Mobile Computing)
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  2. ; ; (, IEEE Transactions on Parallel and Distributed Systems)
    null (Ed.)
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  3. ; ; ; ; (, MM '20: Proceedings of the 28th ACM International Conference on Multimedia)
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  4. ; ; (, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
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  5. ; ; (, 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS))
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  6. ; ; ; (, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence)
    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 general problem by considering a system with 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. Our experiment demonstrates utility improvement about 7 times and time saving of 54% to meet a utility objective compared to the previous works. 
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  7. ; ; ; ; (, Proceedings of the 27th ACM International Conference on Multimedia - MM '19)
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