In this text, strategies for providing personalized recommendations even for users who are interacting with AI-driven products for the first time are discussed. The "cold start problem" is introduced, which refers to the challenge of making accurate recommendations when there is insufficient information about a new user's preferences. Three solutions are proposed: 1) Explore Preferences During Onboarding - asking users explicit questions about their goals, preferences, and interests during registration and their first few interactions with the product; 2) Enrich Profiles for Better Collective Filtering - using metadata gathered during registration and onboarding to suggest products that users with similar profiles like or popular near the user's location; and 3) Exploit Existing Data - leveraging existing data from other sources, such as a customer loyalty card number. Tools like Airbyte can help break data silos and ingest first-party, second-party, and even third-party data to overcome the cold start challenge.