Search engines rank results based on a combination of factors, including relevance, authority, user behavior, and other ranking signals. One of the primary methods for ranking is through algorithms like Google's PageRank, which measures the importance of a page based on the number and quality of links pointing to it.
In addition to link-based signals, modern search engines also incorporate machine learning models that evaluate the relevance of a document to a query. These models take into account factors such as keyword matches, semantic meaning, and user intent. For example, a search for "best pizza places" might prioritize review websites and local business directories over general food blogs.
Search engines may also use personalization, considering a user's search history, preferences, and geographical location to adjust rankings. Signals like click-through rates (CTR), dwell time, and user engagement are also factored into rankings, as they indicate the quality of results and user satisfaction.