With the rapid expansion of freelance workers and tasks, online labor market platforms face a significant challenge with the cold-start problem, which makes it very difficult to effectively match new workers with suitable tasks. To solve this challenge, this paper presents a novel solution termed the Meta-learning ID Embedding Generator (MIG). MIG addresses the cold-start problem in task recommendation systems by efficiently learning suitable ID embeddings for new workers. MIG consists of an initial embedding generator for generating the initial ID embedding, alongside two adaptors designed to iteratively refine this embedding on the worker's competence and interest. The efficacy of this method has been assessed using authentic data sourced from Freelancer.com, a leading online labor marketplace. The empirical findings demonstrate its superiority over state-of-the-art methods when addressing the needs of two challenging user segments: newcomers to the platform and long-inactive users whose bidding records are sparse. MIG can seamlessly integrate into existing task recommendation systems, thereby enhancing their effectiveness, particularly in cold start scenarios.