Content Recommendation System Using Similar User Intermediary

The patent introduces similar users as an intermediary to bridge the gap between new users and the recommendation system. Instead of directly using a new user's viewing history (which is empty), the system finds users with similar characteristics who do have viewing histories, and uses those histories as proxy data to generate recommendations for the new user. This intermediary approach allows the system to maintain high recommendation relevance while adapting to new users without prior interaction data.

The patent segments users into groups based on similar characteristics (demographics, preferences, behavior patterns) rather than treating each user individually. By dividing the large user base into segments with shared traits, the system can generate personalized recommendations more efficiently. Within each segment, users have similar content preferences, so the system can use representative viewing histories from segment members to create recommendations for all members, significantly reducing computational complexity while maintaining high user engagement.