Name
Abstract | ||
|---|---|---|
On-demand taxi-calling platforms often ignore the social engagement of individual drivers. The lack of social incentives impairs the work enthusiasms of drivers and will affect the quality of service. In this paper, we propose to form teams among drivers to promote participation. A team consists of a leader and multiple members, which acts as the basis for various group-based incentives such as competition. We define the Recommendation-based Team Formation (RTF) problem to form as many teams as possible while accounting for the choices of drivers. The RTF problem is challenging. It needs both accurate recommendation and coordination among recommendations, since each driver can be in at most one team. To solve the RTF problem, we devise a Recommendation-Matrix-Based Framework (RMBF). It first estimates the acceptance probability of recommendations and then derives a recommendation matrix to maximize the number of formed teams from a global view. We conduct trace-driven simulations using real data covering over 64,000 drivers and deploy our solution on a large on-demand taxi-calling platform for online evaluations. Experimental results show that RMBF outperforms the greedy-based strategy by forming up to 20% and 12.4% teams in trace-driven simulations and online evaluations, and the drivers who form teams and are involved in the competition have more service time, number of finished orders and income.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3357384.3357869 | Proceedings of the 28th ACM International Conference on Information and Knowledge Management |
Keywords | Field | DocType |
incentive mechanism, recommendation, team formation | Data science,On demand,Information retrieval,Computer science | Conference |
ISBN | Citations | PageRank |
978-1-4503-6976-3 | 2 | 0.38 |
References | Authors | |
0 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingyu Zhang | 1 | 34 | 4.43 |
| Tianshu Song | 2 | 13 | 3.89 |
| Yongxin Tong | 3 | 1095 | 56.54 |
| Zimu Zhou | 4 | 1157 | 61.40 |
| Dan Li | 5 | 2 | 0.38 |
| Wei Ai | 6 | 53 | 4.44 |
| Lulu Zhang | 7 | 2 | 0.38 |
| Guobin Wu | 8 | 51 | 3.43 |
| Yan Liu | 9 | 2551 | 189.16 |
| Jieping Ye | 10 | 6943 | 351.37 |