Abstract
Recently, online pricing receives extensive attentions for addressing more realistic mobile crowdsensing (MCS) applications. However, existing these works mainly focus on finding an optimal price by repeated price experiments of user's acceptance decision, which incurs a desirable regret. In practice, there are also many other factors that will affect these regrets, such contextual features as discrete features and continuous features. In this paper, we investigate the impact of users' context features and corresponding weights on selecting an optimal price. To apply for multi-dimensional contextual features and learned corresponding weights, two ellipsoid pricing based mechanisms are proposed for homogenous and noisy sensing tasks respectively. Finally, extensive simulations show that our mechanisms outweigh than existing mechanisms.
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