DL0015 Cold Start

The cold start problem is the difficulty of making reliable predictions for new entities (such as users, items, or contexts) lacking historical data.
Many deep learning models, especially in recommendation systems, rely on abundant past data to learn meaningful patterns. When a new user or item is introduced, the model struggles because it doesn’t have enough information to produce accurate predictions.

Mitigation Strategies for the Cold Start Problem:
(1) Transfer Learning / Pretrained Models: Use embeddings or models pre-trained on similar tasks to provide a starting point.
(2) Hybrid Recommendation Models: Combine collaborative filtering (CF) and content-based methods.
(3) Active Learning / User Onboarding: Actively gather more data for new entities through user interactions.