Abstract:
The gig economy has facilitated the growth of customized services through digital platforms that connect consumers with service providers. However, the surge in service p...Show MoreMetadata
Abstract:
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.
Published in: IEEE Transactions on Consumer Electronics ( Volume: 70, Issue: 1, February 2024)