Group Task Recommendation in Mobile Crowdsensing: An Attention-Based Neural Collaborative Approach

Collaborative tasks often require the cooperation of multiple individuals to be completed in mobile crowdsensing (MCS). However, previous task recommendations predominantly focused on individuals rather than groups, making them less effective for collaborative tasks. It is crucial to study the collaborative task recommendation problem in MCS. In this work, we propose an Attention-based Neural Collaborative approach (ANC) for group task recommendation. In particular, a grouping method is designed based on participant abilities to form groups that meet the needs of collaborative tasks. Meanwhile, a dual-attention mechanism is constructed to aggregate member preferences and enhance the representation of tasks and groups. The neural network-based collaborative filter mechanism is employed to generate top-$K$K recommendation lists. Experimental results, based on two real-world datasets, demonstrate that ANC outperforms others, validating its effectiveness and feasibility.