Watch: Google’s Ranking Signals
Overview of search ranking factors including popularity signals, PCTR models, semantic relevance, keyword matching, freshness, and various search modes.
Transcript
How does a modern search engine decide which results you see first? It all starts with a base ranking, which is the initial relevance score of a document. From there, several sophisticated adjustments shape the final list. Keyword matching looks at how often your search terms appear, while embedding adjustments measure semantic similarity. For even deeper context, a semantic relevance model helps the system understand nuance and negation.
The system also relies heavily on user behavior. Popularity signals boost documents that get the most interaction. Predicted click-through rate models estimate the likelihood of a user clicking a result, and these can even be personalized using an individual's history once the search system has served at least one hundred thousand queries. Other ranking factors include document freshness, predicted conversions, and manual adjustments to promote or demote specific content.
When it comes to presenting these results, there are three main search modes. You can get a standard list of results, a generative summary written above the list, or a conversational search that supports follow-up questions. For generative AI searches, only the top five results are analyzed. These are shown either as short text snippets or longer, extractive passages. Finally, the system can block adversarial queries, preventing the underlying large language model from generating inappropriate answers.