#5856. A task recommendation framework for heterogeneous mobile crowdsensing

July 2026publication date
Proposal available till 17-05-2025
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Journal’s subject area:
Theoretical Computer Science;
Information Systems;
Hardware and Architecture;
Software;
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Abstract:
Aiming at the problems of low data quality and high incentive costs caused by the low enthusiasm of participants in mobile crowd sensing, a new task recommendation framework is proposed in this paper. First, the participants historical behaviors are analyzed, assuming that user behaviors can be quantified as the users willingness to participate, and the cosine similarity theorem is used to calculate the similarity between participants, thereby constructing a user-hybrid model. Secondly, probabilistic matrix factorization is developed to predict the willingness of participants, and a ranking model is obtained through learn-to-rank algorithm. Finally, a task recommendation list is generated according to the ranking model, which serves as the target participants preferred task list for sensing task recommendation.
Keywords:
Learn to rank; Mobile crowd sensing; Participants willingness; Probability matrix decomposition; Task recommendation

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