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Task recommendation for mobile crowd sensing system based on multi-view user dynamic behavior prediction

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Abstract

Mobile crowd sensing is a data collection model that combines crowdsourcing ideas and mobile device sensing abilities. In the decision-making process of mobile crowd sensing perception behavior, a single type of historical behavior is used to predict the user's single preference tag, so the generalization ability of the model is weak, and the recommendation efficiency is not high. Aiming at the perception problem that the information overload of mobile crowd sensing leads to a significant increase in participants' decision-making costs, this paper proposes an innovative MUDBP prediction method based on Multi-view and social network group behavior to improve the task recommendation in mobile crowd sensing model. Specifically, this method starts from the multi-time behavior sequence, adopts an attention mechanism, sets different weights for different individual behaviors of various users according to the social influence of different users, and calculates the aggregation representation of group user behaviors at additional time granularity. Then, the multi-scale behavior sequence characteristics of a single user are fused with the multi-scale behavior sequence characteristics of a group of users in a social network, and the multi-view embedded behavior sequence characteristics of a single user are extracted. Finally, through multi-label prediction, the preference probability of users to various task types is obtained. Experimental results based on real data sets show that compared with other baseline methods, the proposed method can effectively improve the accuracy of task recommendation and reduce the perceived cost. At the same time, it can effectively deal with the cold start problem.

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Availability of supporting data

The datasets generated during the current study are available from the corresponding author on reasonable request.

Data Availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

All the authors listed have approved the manuscript that is enclosed.

Funding

This present research work was supported by the National Natural Science Foundation of China (61403109, 61202458), the Specialized Research Fund for the Doctoral Program of Higher Education of China (20112303120007) and the Heilongjiang Natural Science Foundation (LH2020F034).

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Authors and Affiliations

  1. College of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China

    Guosheng Zhao

  2. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China

    Xiao Wang, Jian Wang & Jia Liu

Authors
  1. Guosheng Zhao
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  2. Xiao Wang
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  3. Jian Wang
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  4. Jia Liu
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Contributions

Guosheng Zhao, Xiao Wang, Jian Wang and Jia Liu wrote and revised the manuscript together.

Corresponding author

Correspondence to Guosheng Zhao.

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I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.

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We would like to submit the manuscript entitled “Task Recommendation for Mobile Crowd Sensing System Based on Multi-view User Dynamic Behavior Prediction”, which we wish to be considered for publication in “Peer-to -Peer Networking and applications”.

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Zhao, G., Wang, X., Wang, J. et al. Task recommendation for mobile crowd sensing system based on multi-view user dynamic behavior prediction. Peer-to-Peer Netw. Appl. 16, 1536–1550 (2023). https://doi.org/10.1007/s12083-023-01504-x

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