Abstract
Ranking is an important task in machine learning, information retrieval, and data mining. We consider different notions like similarity and density and their role in ranking. Further, we discuss how centrality and diversity are captured in a variety of ranking tasks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsBibliography
Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press
Witten IH, Frank E, Hall MA (2011) Data mining, 3th edn, Morgan Kauffmann
Agrawal R, Sreenivas G (2009) Diversifying search results, WSDM 2009, Barcelona
Karthik N, Murty MN (2012) Obtaining single document summaries using latent Dirichlet allocation, ICONIP 2012. LNCS 7666
Qin L, Zhu X (2013) Promoting diversity in recommendation by entropy Regularizer, IJCAI 2013, Beijing
Sharad N, Aayush M, Murty MN (2018) Fusing diversity in recommendations in heterogeneous information networks, WSDM 2018, Marina Del Rey
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Murty, M.N., Biswas, A. (2019). Ranking. In: Centrality and Diversity in Search. SpringerBriefs in Intelligent Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-24713-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-24713-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24712-6
Online ISBN: 978-3-030-24713-3
eBook Packages: Computer ScienceComputer Science (R0)