Recommendation Algorithm

Although Unich Network supports diverse types of connections (friends, dating, professional networks), it always prioritizes recommending quality connections over quantity. Key factors influencing recommendations include:

• Geographic proximity: Users are prioritized to receive recommendations from people who are geographically close to them, increasing the likelihood of an actual meeting.

• Interest alignment and mutual benefit potential: The system suggests people with shared interests, goals, or values, increasing the likelihood of quality and sustainable meetings.

• Meeting history: Those who have completed many verified meetings receive better recommendations, creating a positive feedback loop where the most active users are connected with other active users. This optimizes for the mutual benefit potential of the network.

• Fraud prevention: The system detects and removes profiles showing signs of abnormal activity or fraud, ensuring that recommendations remain focused on trustworthy real connections that can be rewarded with FC.

Instead of operating on a logic of user retention and attention extraction, Unich Networking builds its algorithms around the real value of each connection. Interactions are prioritized based on relevance and their potential to lead to real-world connections, laying the foundation for a decentralized, transparent, and balanced distribution of FC within the Unich Network economy.