The cold-start problem is the recommender's blind spot at the edges: a brand-new user has no viewing history to learn from, and a brand-new title has no audience signal yet, so behavioral methods like collaborative filtering have little to work with.
Platforms mitigate it from both sides. For new users: onboarding taste prompts, popular and trending defaults, and fast learning from the first few plays. For new titles: content-based filtering from metadata, editorial placement, and similarity to known titles. Handling cold start well matters because the first session shapes a new subscriber's impression - and the first weeks are when churn risk is highest.
The handbook · 7 volumes
Fora Soft Video Engineering Handbook
The cold-start problem hits every new viewer and every new title, because a recommender has little behaviour to learn from yet. We wrote seven volumes covering the video stack; for the cold-start problem begin with Volume 6 on ML basics for product decisions, computer vision, and search over a video archive, then Volume 7 on serving and cost. Written by Nikolay Sapunov, CEO at Fora Soft.
New viewers getting poor picks because of the cold-start problem?
We will review your catalog metadata and recommender and send a cold-start plan using content signals from the video itself. 250+ video products shipped since 2005.
