PBSM: Predictive Bi-Preference Stable Matching in Spatial Crowdsourcing

Task assignment is a fundamental challenge in Spatial Crowdsourcing which aims to assign location-based tasks to workers under spatial-temporal constraints. Recently, some exciting research has introduced the preference of workers and tasks to improve assignment quality. However, they either primarily focus on the current preferences of both workers and tasks or only consider the unilateral prediction-based preference of workers, overlooking the impact of workers' interconnection and tasks' completed sequences. As a result, they gain suboptimal assignment results in most cases. Inspired by this, we propose a novel problem, named the Predictive Bi-preference Stable Match problem (PBSM), with the goal of maximizing the preferences of both workers and tasks by taking into account the social network of workers and task completion sequence. The PBSM problem is proven to be NP-hard. To tackle this challenging problem, we develop a GCN-enhanced Transformer-based Prediction and Bi-preference Stable Matching (GETBM) framework with two stages: the bi-preference prediction stage and the bilateral assignment stage. In the prediction stage, the Worker Preference Model (WPM) and Task Preference Model (TPM) models are presented to predict the worker-to-task (Worker2Task) and task-to-worker (Task2Worker) preference lists, respectively. Then, we design a bilateral preference-aware stable matching (BPM) algorithm and prove it can gain stable results. To generalize to multiple scenarios, three optimization strategies are devised based on spatial-temporal constraints and priority consideration to gain better assignment performance. Extensive experiments are conducted to prove the superiority of the GETBM framework on two real datasets.