Queries given to search engines or other retrieval systems are often not very specific, and lead to a large number of matching documents. In these cases the retrieval system should have a good estimate of the relevance of the documents to the user's needs, so that "good" documents show up early in the enumeration. A large number of factors should enter into a good ranking method, including the positions of the query terms in the document, linguistic context of the matches, link popularity, classification of the documents, user models etc. "Classical" methods compute a mesaure of "distance" between the query and the retrieved document, such as TF/IDF or cosine similarity. For hyperlinked documents, methods which make use of the hyperlink structure have proved very effective for relevance ranking. Google was the first large-scale search engine to make use of hyperlink sructure for relevance ranking.
Relevance; Topical Relevance; Aboutness