Spatial and Temporal Pricing Approach for Tasks in Spatial Crowdsourcing

Jing Qian, Shushu Liu, An Liu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Pricing is an important issue in spatial crowdsourcing (SC). Current pricing mechanisms are usually built on online learning algorithms, so they fail to capture the dynamics of users’ price preference timely. In this paper, we focus on the pricing for task requesters with the goal of maximizing the total revenue gained by the SC platform. By considering the relationship between the price and the task, space, and time, a spatial and temporal pricing framework based task-transaction history is proposed. We model the price of a task as a three-dimensional tensor (task-space-time) and complete the missing entries with the assistant of historical data and other three context matrices. We conduct extensive experiments on a real taxi-hailing dataset. The experimental results show the effectiveness of the proposed pricing framework.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering – WISE 2020 - 21st International Conference, Proceedings
EditorsZhisheng Huang, Wouter Beek, Hua Wang, Yanchun Zhang, Rui Zhou
Number of pages13
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Web Information Systems Engineering - Amsterdam, Netherlands
Duration: 20 Oct 202024 Oct 2020
Conference number: 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12342 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Web Information Systems Engineering
Abbreviated titleWISE


  • Pricing
  • Spatial crowdsourcing
  • Task assignment

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