Similarities are discovered among different users with respect to their media experiences and other behaviors, such as taste in media items (e.g., books, music, movies, magazines, art, etc.), browsing behavior, purchase decisions, and online shopping habits, and usage history. The similarities are determined in part by substantially real-time comparison of individual users with a set of predetermined user-based clusters formed from the experiences and behaviors of sample users. Users from a population may then be identified based on similarity metrics. Recommendations for new media items, or other goods/services, may also be made based on choices being made by similar users.
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