Crowdsourcing is a new paradigm which divides work between participants to achieve a cumulative result. To achieve good service quality for a crowdsourcing system, incentive mechanisms are necessary to attract more user participation. Most of existing mechanisms apply only for the crowdsourcing scenario where the platform user will employ the workers to perform certain tasks to maximize one's utility with budget constraint. However, the budget is not fixed in practice, and the final goal for the platform user is to achieve profit maximization. In this paper, we consider a more general optimization objective for the budget-free platform user, profit maximizing, i.e., the difference between her utility and the total reward to the participants. We study the problem of how to maximize the profit in a crowdsourcing activity where the platform user's proceeds is a symmetric submodular demand valuation function and the users' cost information are prior-free. Based on the framework of random sampling and profit extraction, we propose a mechanism which is computationally tractable, truthful, individually rational and constant-factor competitive to the optimal profit omniscient single-price auction in a fixed market. We also extend the profit extract algorithm to the online case. By this work, we enrich the class of competitive auctions by considering a more general optimization objective and a more general demand valuation function in both the offline and online platform for the crowdsourcing system.
展开▼
