AI-driven efficient matching mechanism
The platform's core matching algorithm is based on three dimensions of data modeling:
- Skill labeling system: Users are required to detail specialized competencies such as 'front-end developer', 'growth hacker', etc.
- experience matrix: Automatically analyze quantitative indicators such as years of experience, success stories, etc.
- Target synergies: Parsing the match between the needs of both parties through the 'Looking For' field
The matching process supports multi-criteria filtering, including 15 filtering dimensions such as geographic location and project stage. Paid users can get priority display rights, and the exposure rate of their profiles in the search results is increased by 300%. The system updates the recommendation list daily, and continuously optimizes the recommendation accuracy based on user behavior data.
The technical architecture uses collaborative filtering algorithms combined with NLP to process the project description text to ensure that the recommendation results meet both the hard condition requirements and the soft collaborative needs.
This answer comes from the articleIndieMerger: a platform for AI smart matching of startup partnersThe
