Manual Monthly Maintenance:
As per internal practices and product training guidelines, taxonomy data is manually refreshed on a monthly basis. This update cycle ensures that the taxonomy remains accurate, relevant, and aligned with evolving industry standards.
Monthly Taxonomy Refresh Includes:
- Enrichment of new skills, tools, and job profiles
- Updates to existing terms to reflect industry-standard changes
- Multilingual expansions to support global user bases
- Alignment with government databases for international compliance
- Enhancement of multilingual support for improved parsing and search accuracy
Scoring Logic Behind Search & Match:
The scoring mechanism used in Search & Match operates in two layers:
KC Link :- https://docs.rchilli.com/kc/search_match_endpoints_overview
A. Default Logic (Baseline)
- This includes basic weights for attributes like skills, location, education, domain, etc.
- It works out-of-the-box with predefined weightage.
- Ideal for first-time users or customers not requiring customization.
B. Configurable Logic (Customized Weights)
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Clients can manually adjust weightage on the front end, influencing:
- Emphasis on education, location proximity, or prior employer preferences.
- Changes in the front-end dynamically affect the scoring returned via API, though the core logic and scoring engine backend remains intact.
The Search & Match Engine 3.0 enables this dynamic scoring and ranking mechanism, allowing customers to fine-tune candidate-job match results based on their business priorities.
The scoring logic is not entirely static—your front-end configurations (e.g., giving 60% weight to “Skills” vs. 10% to “Location”) directly influence the final match score.
Help Link:-
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