Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on the problem recently. MongoDB Detailed vendor-provided information available Document, Multi-model Document store, Search engine: 457.22-0.51 +30.26; 6. IBM Db2 Detailed vendor-provided information available Relational, Multi-model Relational DBMS, Document store, RDF store: 157.17-3.26-11.53; 7. Redis Detailed vendor-provided information available.

Abstract

NOTE ⁃ A New Edition of This Title is Available: Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition

Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing approaches, theories, applications, and future work.

The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings.

Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting SVM, Neural Network based approaches.

The author also introduces some popular learning to rank methods in details. These include PRank, OC SVM, Ranking SVM, IR SVM, GBRank, RankNet, LambdaRank, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, Borda Count, Markov Chain, and CRanking.

The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation.

A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed.

Table of Contents: Introduction / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work

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Editor's note: October 2020

After the publication of the World University Rankings 2021, National Research Nuclear University MEPhI informed us that they had submitted, and signed off, incorrect academic staff, international academic staff, student and international student numbers in Life Sciences and Physical Sciences in THE’s data portal. This matter was reviewed under our corrections policy, and we agreed, at our discretion, to re-calculate MEPhI’s score based on a corrected data submission. The revision has not affected the university’s ranking position overall. The university’s corrected position in the Physical Sciences subject ranking is 98.

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After the publication of the World University Rankings 2021, we were informed that an affiliation had been incorrectly assigned in the Elsevier bibliometric profile of Government College Women University Faisalabad, resulting in their inclusion in the ranking. After recalculating the publication count based on the correct affiliations, Government College Women University Faisalabad did not reach the publication threshold and therefore should not have been included in the World University Rankings. This matter was reviewed under our corrections policy and we have removed the institution from the ranking.

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Editor's note: September 2020

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Information Rank

After the publication of the World University Rankings 2021, Shahid Chamran University of Ahvaz informed us that they had submitted, and signed off, incorrect income numbers into THE’s data portal. This matter was reviewed under our corrections policy, and we agreed, at our discretion, to re-calculate Shahid Chamran University of Ahvaz’s score based on a corrected data submission. The revision has not affected the university’s position overall.

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