ABSTRACT
The pervasiveness of GPS-enabled devices and wireless communication technologies flourish the market of Spatial Crowdsourcing (SC), which consists of location-based tasks and requires workers to physically be at specific locations to complete them. In this work, we study the problem of Worker Churn based Task Assignment in SC, where tasks are to be assigned by considering workers' churn. In particular, we aim to achieve the highest total rewards of task assignments based on the worker churn prediction. To solve the problem, we propose a two-phase framework, which consists of a worker churn prediction phase and a task assignment phase. In the first phase, we use an LSTM-based model to extract the latent feelings of workers based on the historical data and then estimate the idle time intervals of workers. In the assignment phase, we design an efficient greedy algorithm and a Kuhn-Munkras (KM)-based algorithm that can achieve the optimal task assignment. Extensive experiments offer insight into the effectiveness and efficiency of the proposed solutions.
- Jaehuyn Ahn, Junsik Hwang, Doyoung Kim, Hyukgeun Choi, and Shinjin Kang. A survey on churn analysis in various business domains. Access, 8:220816--220839, 2020.Google ScholarCross Ref
- Adnan Amin, F. Al-Obeidat, B. Shah, May Al Tae, C. Khan, Hamood Ur Rehman Durrani, and S. Anwar. Just-in-time customer churn prediction in the telecommunication sector. J SUPERCOMPUT, 76:3924--3948, 2017.Google ScholarCross Ref
- Alejandro Correa Bahnsen, Djamila Aouada, and B. Ottersten. A novel cost-sensitive framework for customer churn predictive modeling. Decision Analytics, 2:1--15, 2015.Google ScholarCross Ref
- Z. Chen, P. Cheng, Y. Zeng, and L. Chen. Minimizing maximum delay of task assignment in spatial crowdsourcing. ICDE, pages 1454--1465, 2019.Google ScholarCross Ref
- Z. Chen, Peng Cheng, Liquan Chen, Xuemin Lin, and C. Shahabi. Fair task assignment in spatial crowdsourcing. PVLDB, 13:2479--2492, 2020. Google ScholarDigital Library
- P. Cheng, L. Chen, and J. Ye. Cooperation-aware task assignment in spatial crowdsourcing. ICDE, pages 1442--1453, 2019.Google ScholarCross Ref
- P. Cheng, Xiang Lian, Lei Chen, and C. Shahabi. Prediction-based task assignment in spatial crowdsourcing. ICDE, pages 997--1008, 2017.Google ScholarCross Ref
- P. Cheng, Xiang Lian, Xun Jian, and Lei Chen. Frog: A fast and reliable crowdsourcing framework. TKDE, 31:894--908, 2019.Google ScholarCross Ref
- Peng Cheng, Xiang Lian, Z. Chen, L. Chen, J. Han, and J. Zhao. Reliable diversity-based spatial crowdsourcing by moving workers. PVLDB, 8:1022--1033, 2015. Google ScholarDigital Library
- Y. Cheng, Boyang Li, Xiangmin Zhou, Y. Yuan, G. Wang, and L. Chen. Real-time cross online matching in spatial crowdsourcing. ICDE, pages 1--12, 2020.Google ScholarCross Ref
- Alae Chouiekh and E. Haj. Deep convolutional neural networks for customer churn prediction analysis. Int. J. Cogn. Informatics Nat. Intell., 14:1--16, 2020.Google ScholarDigital Library
- Yue Cui, Liwei Deng, Yan Zhao, Bin Yao, Vincent W Zheng, and Kai Zheng. Hidden poi ranking with spatial crowdsourcing. In KDD. Google ScholarDigital Library
- Y. Le Cun, L. Jackel, B. Boser, J. Denker, H. Graf, I. Guyon, D. Henderson, R. Howard, and W. Hubbard. Handwritten digit recognition: applications of neural network chips and automatic learning. COMMUN MAG, 27:41--46, 1989. Google ScholarDigital Library
- H. Dang, Tuan A. Nguyen, and Hien To. Maximum complex task assignment: Towards tasks correlation in spatial crowdsourcing. In IIWAS '13, page 77--81, 2013. Google ScholarDigital Library
- Dingxiong Deng, Cyrus Shahabi, and Ugur Demiryurek. Maximizing the number of worker's self-selected tasks in spatial crowdsourcing. In SIGSPATIAL, pages 324--333, 2013. Google ScholarDigital Library
- Dingxiong Deng, Cyrus Shahabi, and Linhong Zhu. Task matching and scheduling for multiple workers in spatial crowdsourcing. In SIGSPATIAL, pages 1--10, 2015. Google ScholarDigital Library
- J. Dias, P. Godinho, and Pedro Torres. Machine learning for customer churn prediction in retail banking. ICCSA, 12251:576--589, 2020.Google Scholar
- S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Computation, 9:1735--1780, 1997.Google ScholarDigital Library
- A. Hudaib, Reham Dannoun, Osama Harfoushi, Ruba Obiedat, and Hossam Faris. Hybrid data mining models for predicting customer churn. IJCNS, 08:91--96, 2015.Google ScholarCross Ref
- Marcel Karnstedt, Matthew Rowe, Jeffrey Chan, H. Alani, and Conor Hayes. The effect of user features on churn in social networks. In WebSci '11, 2011. Google ScholarDigital Library
- Leyla Kazemi and Cyrus Shahabi. Geocrowd:enabling query answering with spatial crowdsourcing. In International Conference on Advances in Geographic Information Systems, pages 189--198, 2012. Google ScholarDigital Library
- Leyla Kazemi, Cyrus Shahabi, and Lei Chen. Geotrucrowd:trustworthy query answering with spatial crowdsourcing. In SIGSPATIAL, pages 314--323, 2013. Google ScholarDigital Library
- Stefan M. Kostic, Mirjana Simic, and Miroljub V. Kostic. Social network analysis and churn prediction in telecommunications using graph theory. Entropy, 22, 2020.Google Scholar
- Xiang Li, Yan Zhao, Jiannan Guo, and Kai Zheng. Group task assignment with social impact-based preference in spatial crowdsourcing. In DASFAA, pages 677--693, 2020.Google ScholarDigital Library
- Xiang Li, Yan Zhao, Xiaofang Zhou, and Kai Zheng. Consensus-based group task assignment with social impact in spatial crowdsourcing. Data Science and Engineering, 5(4):375--390, 2020.Google ScholarCross Ref
- D. Montgomery and Elizabeth A. Peck. Introduction to linear regression analysis. 2001. Google ScholarDigital Library
- Wangze Ni, P. Cheng, L. Chen, and Xuemin Lin. Task allocation in dependency-aware spatial crowdsourcing. ICDE, pages 985--996, 2020.Google ScholarCross Ref
- Tianshu Song, Ke Xu, Jiangneng Li, Yiming Li, and Yongxin Tong. Multi-skill aware task assignment in real-time spatial crowdsourcing. GeoInformatica, 24:153--173, 2019.Google ScholarDigital Library
- Qian Tao, Yongxin Tong, Zimu Zhou, Yexuan Shi, L. Chen, and K. Xu. Differentially private online task assignment in spatial crowdsourcing: A tree-based approach. ICDE, pages 517--528, 2020.Google ScholarCross Ref
- Hien To, C. Shahabi, and Li Xiong. Privacy-preserving online task assignment in spatial crowdsourcing with untrusted server. ICDE, pages 833--844, 2018.Google ScholarCross Ref
- Yongxin Tong, Yu xiang Zeng, Bolin Ding, L. Wang, and L. Chen. Two-sided online micro-task assignment in spatial crowdsourcing. Transactions on Knowledge and Data Engineering, 33:2295--2309, 2021.Google Scholar
- Yongxin Tong, Zimu Zhou, Yuxiang Zeng, L. Chen, and C. Shahabi. Spatial crowdsourcing: a survey. VLDB J, 29:217--250, 2019.Google ScholarCross Ref
- Meng Xi, Zhiling Luo, N. Wang, and Jianwei Yin. A latent feelings-aware rnn model for user churn prediction with behavioral data. ArXiv, abs/1911.02224, 2019.Google Scholar
- Jinfu Xia, Yan Zhao, Guanfeng Liu, Jiajie Xu, Min Zhang, and Kai Zheng. Profit-driven task assignment in spatial crowdsourcing. In IJCAI, pages 1914--1920, 2019. Google ScholarDigital Library
- Yan Zhao, Jiannan Guo, Xuanhao Chen, Jianye Hao, Xiaofang Zhou, and Kai Zheng. Coalition-based task assignment in spatial crowdsourcing. In ICDE, pages 241--252, 2021.Google ScholarCross Ref
- Yan Zhao, Yang Li, Yu Wang, Han Su, and Kai Zheng. Destination-aware task assignment in spatial crowdsourcing. In CIKM, pages 297--306, 2017. Google ScholarDigital Library
- Yan Zhao, J. Xia, G. Liu, Han Su, Defu Lian, Shuo Shang, and Kai Zheng. Preference-aware task assignment in spatial crowdsourcing. In AAAI, pages 2629--2636, 2019.Google ScholarCross Ref
- Yan Zhao, Kai Zheng, Yue Cui, Han Su, Feida Zhu, and Xiaofang Zhou. Predictive task assignment in spatial crowdsourcing: a data-driven approach. In ICDE, pages 13--24, 2020.Google ScholarCross Ref
- Yan Zhao, Kai Zheng, Jiannan Guo, Bin Yang, Torben Bach Pedersen, and Christian S Jensen. Fairness-aware task assignment in spatial crowdsourcing: Game-theoretic approaches. In ICDE, pages 265--276, 2021.Google ScholarCross Ref
- Yan Zhao, Kai Zheng, Yang Li, Han Su, Jiajun Liu, and Xiaofang Zhou. Destination-aware task assignment in spatial crowdsourcing: A worker decomposition approach. TKDE, pages 2336--2350, 2019.Google ScholarCross Ref
- Yan Zhao, Kai Zheng, Hongzhi Yin, Guanfeng Liu, Junhua Fang, and Xiaofang Zhou. Preference-aware task assignment in spatial crowdsourcing: from individuals to groups. TKDE, 2020.Google ScholarCross Ref
- Libin Zheng and Lei Chen. Multi-campaign oriented spatial crowdsourcing. TKDE, 32:700--713, 2020.Google ScholarCross Ref
Index Terms
- Task Assignment with Worker Churn Prediction in Spatial Crowdsourcing
Recommendations
Loyalty-based Task Assignment in Spatial Crowdsourcing
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementWith the fast-paced development of mobile networks and the widespread usage of mobile devices, Spatial Crowdsourcing (SC) has drawn increasing attention in recent years. SC has the potential for collecting information for a broad range of applications ...
Privacy-Preserving Batch-based Task Assignment in Spatial Crowdsourcing with Untrusted Server
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementIn this paper, we study the privacy-preserving task assignment problem in spatial crowdsourcing, where the locations of both workers and tasks, prior to their release to the server, are perturbed with Geo-Indistinguishability (a differential privacy ...
Extra-Budget Aware Task Assignment in Spatial Crowdsourcing
Web Information Systems Engineering – WISE 2021AbstractWith the prevalence of sharing economy and mobile Internet, spatial crowdsourcing (SC) has been receiving increased attentions recently. A core issue in SC is task assignment, which aims to assign tasks to suitable workers. As workers need to ...
Comments