Design an algorithmic E-E-A-T scoring system
Tests turning subjective quality into signals. Split E-E-A-T into distinct feature families, combine structured metadata with unstructured NLP and graph signals, and calibrate against human rater labels. Red flag: one opaque score or CTR as trust proxy.
Tests whether a senior engineer can decompose a vague quality rubric into a production feature pipeline. A strong answer maps each E-E-A-T pillar to specific structured fields and unstructured signals, uses a knowledge graph for author and publisher disambiguation, and explicitly defines ground truth via Search Quality Rater guidelines rather than engagement metrics.
Read the original → developers.google.com
- #eeat
- #search ranking
- #feature engineering
- #information retrieval
- #system design
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