📅 2025-06-05 — Session: Design and Implementation of SERP Domain Scoring System
🕒 03:25–04:25
🏷️ Labels: Scoring System, SERP, ATS, Python, Data Processing
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to design and implement a scoring system for Search Engine Results Page (SERP) domains, focusing on their relevance to employer Applicant Tracking Systems (ATS) or direct application pages.
Key Activities
- Designing a Scoring System: Developed a structured approach for ranking SERP domains, including heuristics for scoring and integration with a DataFrame.
- Critical Evaluation: Assessed potential edge cases and suggested improvements to enhance the scoring system’s robustness and accuracy.
- Domain Categorization: Established a framework for categorizing domains related to ATS, job boards, and employer sites.
- Implementation: Created a Python function for labeling domains using dictionaries and applied these labels to a DataFrame.
- Unknown Domains Categorization: Analyzed and categorized unknown domains into existing labels and provided recommendations for integration.
- Ranking Job Aggregators: Ranked job aggregators by effectiveness and proposed labels and scores.
- Refinement: Refined the labeling and scoring function for domains to ensure consistent categorization.
Achievements
- Successfully designed and implemented a comprehensive scoring system for SERP domains.
- Enhanced the categorization framework for ATS and job boards.
- Improved the labeling and scoring functions for better data integration.
Pending Tasks
- Further review and testing of the scoring system to handle additional edge cases.
- Continuous refinement of domain categorization strategies.