Personalized Recommendation System Based on Meta-Learning for Human-Centric Consumer Services in Gig Economy

The gig economy has facilitated the growth of customized services through digital platforms that connect consumers with service providers. However, the surge in service providers has led to a “cold-start problem”, which limits the effectiveness of personalized task recommendation systems. To address this challenge, this paper purposed a personalized recommendation system for human-centric consumer services in the gig economy. It addresses the problem by using meta-learning to generate suitable preference embeddings for workers with limited bidding history, interests, and working competence. The system includes a competence module with self-attention and interest modules to capture workers’ personalized preferences. The model is evaluated on real-world datasets from Freelancer.com, and the results demonstrate that it outperforms state-of-the-art models in accurately recommending suitable personalized tasks to both new and existing workers with skill-evolving. The proposed system can reduce task completion times and improve task quality by ensuring that tasks are assigned to the most suitable workers.