What is the cold start problem in recommendations?

Cold start is when a recommender has too little data about a new customer or a new product to recommend well.

A recommender learns from history, so a first-time visitor or a newly listed product gives it little to work with. Common fixes include using product attributes and descriptions, context such as location or season, popular items, and simply asking the customer.

Some systems live with cold start permanently. Google described its Discover feed as being in “eternal cold start” because it focuses on fresh content that has no history yet. Plain-language preference profiles and conversational assistants are also being used to close the gap faster, because a customer can say in one sentence what would otherwise take many clicks to infer.