Information Gain Math: Optimizing Content Structure to Pass Google's Vector Originality Scoring Algorithms

핵심 내용

1. Understand the patent. Information gain measures the reduction in uncertainty a document provides over previously seen documents.
2. Add unique data. Original measurements, pricing, availability, or experience that competitors lack.
3. Structure for extraction. Use clear headings, lists, and tables so crawlers can identify new facts.
4. Avoid boilerplate. Repeated intros and conclusions dilute the signal.
5. Cite sources. Linking to primary sources supports the new facts you present.
6. Update regularly. Fresh data maintains information gain over time.
7. Test against competitors. Compare your page's facts to the top results; remove overlaps.
8. Measure impact. Track rankings before and after adding unique content.

Architecture

Information gain is computed by comparing the document's vector representation against the user's accumulated context. Pages that add new dimensions to the context score higher. Structure and unique data increase the likelihood of high information gain.

Production configuration

from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer('all-MiniLM-L6-v2')

def information_gain_score(new_page, top_results):
    new_vec = model.encode([new_page])[0]
    result_vecs = model.encode(top_results)
    # cosine similarity to nearest result
    sims = np.dot(result_vecs, new_vec) / (np.linalg.norm(result_vecs, axis=1) * np.linalg.norm(new_vec))
    max_sim = np.max(sims)
    # lower similarity = higher information gain
    return 1.0 - max_sim

score = information_gain_score(my_page, competitor_pages)
print(f"Information gain score: {score:.2f}")

import re

def extract_facts(text):
    # simple sentence-level fact extraction
    sentences = re.split(r'(?<=[.!?])\s+', text)
    facts = [s for s in sentences if len(s.split()) > 5]
    return facts

new_facts = extract_facts(my_page)
comp_facts = extract_facts('\n'.join(competitor_pages))
unique = [f for f in new_facts if f not in comp_facts]
print(f"Unique facts: {len(unique)}")

# check heading structure
curl -s https://example.com/page | grep -oE '<h[1-6][^>]*>[^<]*</h[1-6]>'
# check for duplicate intro patterns
grep -c 'In conclusion\|Furthermore\|Moreover' page.html

Edge cases from production

• Adding unique data that is wrong hurts more than repeating correct facts.
• Information gain is query-dependent; the same page may score differently for different queries.
• Structured data helps crawlers extract facts; unstructured prose may be ignored.
• Updating old pages can restore information gain; stale content loses it.

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