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PageRank is a way of measuring the importance of website pages. According to Google: PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites. [1]
In information retrieval, Okapi BM25 (BM is an abbreviation of best matching) is a ranking function used by search engines to estimate the relevance of documents to a given search query.
Fig.1. Google matrix of Wikipedia articles network, written in the bases of PageRank index; fragment of top 200 X 200 matrix elements is shown, total size N=3282257 (from [1]) A Google matrix is a particular stochastic matrix that is used by Google's PageRank algorithm. The matrix represents a graph with edges representing links between pages.
Ranking of query is one of the fundamental problems in information retrieval (IR), [1] the scientific/engineering discipline behind search engines. [2] Given a query q and a collection D of documents that match the query, the problem is to rank, that is, sort, the documents in D according to some criterion so that the "best" results appear early in the result list displayed to the user.
For example, PageRank or document's length. Such features can be precomputed in off-line mode during indexing. Such features can be precomputed in off-line mode during indexing. They may be used to compute document's static quality score (or static rank ), which is often used to speed up search query evaluation.
Google PageRank (Google PR) is one of the methods Google uses to determine a page's relevance or importance. Important pages receive a higher PageRank and are more likely to appear at the top of the search results. Google PageRank (PR) is a measure from 0 - 10. Google PageRank is based on backlinks.
Discounted cumulative gain (DCG) is a measure of ranking quality in information retrieval.It is often normalized so that it is comparable across queries, giving Normalized DCG (nDCG or NDCG).
If their relative mass value exceeds the threshold, the documents are considered to be spam. A second threshold for the PageRank values of the selected documents is applied. Only high PageRank documents are labelled as spam. The purpose of the methodology is to identify spam documents with artificially inflated PageRank values.