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Ranking (information retrieval)

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Ranking Algorithms
PageRank: Originated in the 1940s, developed by Wassily Leontief in economics.
– Endorsement Ranking: Developed by Charles H Hubbell in 1965, based on the importance of endorsers.
– Journal Ranking: Developed by Gabriel Pinski and Francis Narin, based on citations from important journals.
– HITS Algorithm: Developed by Jon Kleinberg, treating web pages as hubs and authorities.

Ranking Models
– Boolean Model: Fetches complete matches, does not rank documents.
– Vector Space Model: Addresses partial matches, assigns weights to index items, calculates similarity scores using cosine similarity.
– Probabilistic Model: Uses probability theory, ranks documents based on decreasing probability of relevance.

Evaluation Measures
– Precision: Measures the proportion of top-ranked results that are relevant.
– Recall: Measures the completeness of the information retrieval process.
– F1 Score: Combines precision and recall into a harmonic mean.
– Precision-Recall Curves: Plotted to evaluate ranked retrieval results.

HITS Algorithm
– HITS uses Link Analysis for analyzing page relevance.
– Works on small sets of subgraph.
– Query dependent.
– Subgraphs are ranked according to weights in hubs and authorities.
– Pages with the highest ranks are fetched and displayed.

Learning to Rank: Application of Machine Learning
– Learning to rank is an application of machine learning.
– Used for solving the ranking problem.
– Machine learning techniques are applied to rank items.
– Widely used in information retrieval.
– Learning to rank improves the accuracy of search results.

Ranking of query is one of the fundamental problems in information retrieval (IR), the scientific/engineering discipline behind search engines. 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. Ranking in terms of information retrieval is an important concept in computer science and is used in many different applications such as search engine queries and recommender systems. A majority of search engines use ranking algorithms to provide users with accurate and relevant results.

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