Some successful solutions to the Cold Start Problem include the use of collaborative filtering, content-based filtering, and hybrid methods. Collaborative filtering uses the behavior of other users to recommend items to the new user, while content-based filtering uses the properties of items to recommend similar items. Hybrid methods combine these two approaches. Another solution is to use popularity-based recommendations, where the most popular items are recommended to new users. Finally, some platforms use a technique called 'onboarding' where they ask new users to rate a few items to build an initial profile.
When a networked product launches, it faces a chicken-and-egg problem: people need to use it for it...
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