Cold Start

The "cold start" problem in machine learning refers to the difficulty of training models when limited or no data is available for new users, items, or tasks. Current research focuses on mitigating this challenge through various techniques, including Bayesian methods, multimodal embedding networks, and transfer learning approaches leveraging knowledge graphs or pre-trained language models. These advancements are crucial for improving the performance of recommender systems, search engines, and other applications that rely on user interactions, ultimately enhancing user experience and business outcomes.