Understanding Google's ABC Ranking Signals: How Search Results Are Shaped

The article from Search Engine Land, titled "The ABCs of Google Ranking Signals", explores how Google ranks web pages, based on information from a recent U.S. Department of Justice trial and interviews with Google engineers Pandu Nayak and HJ Kim.

Key Points:

• Ranking Signals: Google uses over 100 raw signals (basic data points) and combines them into top-level signals to evaluate and rank content.
• Machine Learning: Google's ranking includes machine learning models like RankEmbed, which maps queries and documents into a semantic space to improve matching relevance.
• Classic Signals Still Matter: Traditional factors such as PageRank and user engagement (e.g. clicks, dwell time) remain important in determining rank.
• Navboost: This system uses 13 months of historical click data to help rank pages. It’s not an AI system but a large data table based on user behaviour.
• Twiddlers: These are re-ranking tools that tweak search results based on freshness, relevance, and user preferences.
• The ABC Signals:
  • A – Anchors: Link texts from other websites.
  • B – Body: The actual on-page content.
  • C – Clicks: User interactions, indicating satisfaction or relevance.

In summary, Google’s ranking system is a hybrid of traditional information retrieval and modern machine learning, with a strong focus on user experience.