Task Recommendation in Spatial Crowdsourcing: A Trade-Off Between Diversity and Coverage

The popularity of mobile devices has led to the increased attention of Spatial Crowdsourcing (SC), a framework that assigns location-sensitive tasks to mobile workers. Task recommendation is crucial in helping workers discover attractive tasks. Existing studies have focused on modeling workers' preferences from past task-performing patterns, but their performance is sub-optimal due to the strong coupling of sequentiality, spatiality, and temporality. Moreover, achieving the highest preference-based utility of workers in most of the existing task recommendation studies is inferior to the benefits of the SC platform and the satisfaction of workers in a long range, due to the lower task coverage rate and the poor diversity in a worker's recommended list. To address these problems, we propose a Diversity-Coverage Balanced Task Recommendation (DCBTaskRec) framework. Specifically, we first introduce a decoupled worker preference learning model that adopts self-attention networks as the backbone and decouples the modeling of multiple factors in attention scores. Additionally, we provide an optimal diveristy-aware approach to maximize the recommendation diversity while keeping high preference-based utility of workers to satisfy the multiple tastes of workers. From the side of the SC platform, we also provide two approaches (i.e., greedy coverage-aware approach and diversity-coverage balanced approach) to achieve high coverage and provide a trade-off between diversity and coverage, respectively. Extensive experiments offer insight into the effectiveness of the proposed framework.