Towards Personalized Task Matching in Mobile Crowdsensing via Fine-Grained User Profiling

In mobile crowdsensing, finding the best match between tasks and users is crucial to ensure both the quality and effectiveness of a crowdsensing system. Existing works usually assume a centralized task assignment by the platform, without addressing the need of fine-grained personalized task matching. In this paper, we argue that it is essential to match tasks to users based on a careful characterization of both the users' preferences and reliability levels. To that end, we propose a personalized task recommender system for mobile crowdsensing, which recommends tasks to users based on a recommendation score that jointly takes each user's preference and reliability into consideration. We first present a simple but effective method to profile the users' preferences by exploiting the implicit feedback from their historical performance. Then, to profile the users' reliability levels, we formalize the problem as a semi-supervised learning model, and propose an efficient block coordinate descent algorithm to solve the problem. For some tasks that lack historical information, we further propose a matrix factorization method to infer the users' reliability on those tasks. We conduct extensive experiments to evaluate the performance of our system, and the evaluation results demonstrate that our system can achieve superior performance to our benchmarks in both user profiling and personalized task matching.