Journal
IEEE TRANSACTIONS ON MOBILE COMPUTING
Volume 20, Issue 10, Pages 2961-2976Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2020.2993963
Keywords
Task analysis; Reliability; Crowdsensing; Sensors; Recommender systems; Measurement; Mobile computing; Mobile crowdsensing; task matching; user profiling; truth discovery; recommender system
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In this paper, a personalized task recommender system for mobile crowdsensing is proposed, which recommends tasks to users based on a recommendation score that considers each user's preference and reliability. The system characterizes users' preference with a hybrid preference metric using implicit feedback, and profiles users' reliability levels with a semi-supervised learning model and an efficient block coordinate descent algorithm. Evaluations show that the system outperforms benchmarks in user profiling and personalized task recommendation.
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.
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