In a Mobile Edge Computing (MEC) environment, as massive amounts of data are generated by user-end devices, the need to alleviate network backhaul pressure by offloading tasks to edge base stations closer to users is becoming increasingly critical. However, the dynamic nature of user mobility and varying interests within and across socially-connected user communities pose significant challenges in designing efficient pre-allocation strategies in MEC. In reality, MEC users can be socially connected and thus share common interests for task types. Consequently, we believe that the group interests of socially-connected MEC users can be exploited and propose a novel collaborative and group interest-informed resource pre-allocation, i.e., DeCoPre. It integrates the decentralized architecture that naturally divides the region into smaller zones for mitigating the impacts of the Single Point of Failure, a self-attention model for capturing multi-user interests and mobility patterns, a collaborative filtering approach for refining prediction results, and a grouping-based technique for resource pre-allocation algorithm. Numerical results upon real-world datasets clearly demonstrate that DeCoPre beats its peers across multiple performance metrics.