In-Route Task Selection in Crowdsourcing

TL;DR

This paper addresses the problem of selecting optimal tasks for traveling workers in crowdsourcing, proposing exact and heuristic solutions to efficiently identify diverse, high-reward task sets along a worker’s route.

Contribution

It introduces the In-Route Task Selection (IRTS) problem, analyzes its NP-hardness, and offers practical heuristic algorithms for real-time task selection.

Findings

Exact solution is computationally expensive for large instances.

Heuristic solutions achieve high precision and recall.

Practical query times are often sub-second.

Abstract

One important problem in crowdsourcing is that of assigning tasks to workers. We consider a scenario where a worker is traveling on a preferred/typical path (e.g., from school to home) and there is a set of tasks available to be performed. Furthermore, we assume that: each task yields a positive reward, the worker has the skills necessary to perform all available tasks and he/she is willing to possibly deviate from his/her preferred path as long as he/she travels at most a total given distance/time. We call this problem the In-Route Task Selection (IRTS) problem and investigate it using the skyline paradigm in order to obtain the exact set of non-dominated solutions, i.e., good and diverse solutions yielding different combinations of smaller or larger rewards while traveling more or less. This is a practically relevant problem as it empowers the worker as he/she can decide, in real time, which tasks suit his/her needs and/or availability better. After showing that the IRTS problem is NP-hard, we propose an exact (but expensive) solution and a few others practical heuristic solutions. While the exact solution is suitable only for reasonably small IRTS instances, the heuristic solutions can produce solutions with good values of precision and recall for problems of realistic sizes within practical, in fact most often sub-second, query processing time.