  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 crowdsensing
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' preference and
reliability. 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 hybrid preference metric to characterize
users' preference by exploiting their implicit feedback. Then, to profile
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 users' historical information, we
further propose a matrix factorization method to infer the users' reliability
levels 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 the benchmarks in both user
profiling and personalized task recommendation.
