Profile-Free and Real-Time Task Recommendation in Mobile Crowdsensing

Guisong Yang, Yanting Li, Xingyu He, Yan Song, Jiangtao Wang, Ming Liu

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)
    110 Downloads (Pure)


    As a key research issue in mobile crowdsensing (MCS), recent studies on task recommendation have begun to focus on recommending tasks to participants according to the learned participant preferences. The common drawbacks of these studies are that, on the one hand, the factors affecting participant preferences are predefined, which is not practical as the influential factors are quite complex and a full map of participant profiles needs to be preexisted. On the other hand, they do not consider how to update the recommendation dynamically. To overcome these drawbacks, a profile-free and real-time task recommendation method is proposed in this work. First, we apply the recommendation systems to MCS to realize profile-free task recommendations. Second, a participant-task-location tensor is constructed, based on which an improved tensor factorization method is presented to provide task recommendations for participants at a given location. Finally, we design a real-time update algorithm based on the idea of one update at a time to update task recommendation lists for participants in real time. Based on real-world trace data sets, extensive evaluations show that the proposed method has obvious advantages over other baselines in terms of accuracy and time cost.

    Original languageEnglish
    Pages (from-to)1311-1322
    Number of pages12
    JournalIEEE Transactions on Computational Social Systems
    Issue number6
    Early online date29 Apr 2021
    Publication statusPublished - 1 Dec 2021

    Bibliographical note

    © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


    Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 61802257 and Grant 61602305 and in part by the Natural Science Foundation of Shanghai under Grant 18ZR1426000 and Grant 19ZR1477600.


    • Crowdsensing
    • Matrix decomposition
    • Mobile crowdsensing (MCS)
    • profile-free
    • real-time
    • Real-time systems
    • Resource management
    • Sensors
    • Task analysis
    • task recommendation.
    • Tensors

    ASJC Scopus subject areas

    • Modelling and Simulation
    • Social Sciences (miscellaneous)
    • Human-Computer Interaction


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