• M. N. MurtyEmail author
  • Anirban Biswas
Part of the SpringerBriefs in Intelligent Systems book series (BRIEFSINSY)


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.


Similarity Search engine Centrality Diversity 


  1. 1.
    Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University PressGoogle Scholar
  2. 2.
    Witten IH, Frank E, Hall MA (2011) Data mining, 3th edn, Morgan KauffmannGoogle Scholar
  3. 3.
    Agrawal R, Sreenivas G (2009) Diversifying search results, WSDM 2009, BarcelonaGoogle Scholar
  4. 4.
    Karthik N, Murty MN (2012) Obtaining single document summaries using latent Dirichlet allocation, ICONIP 2012. LNCS 7666Google Scholar
  5. 5.
    Qin L, Zhu X (2013) Promoting diversity in recommendation by entropy Regularizer, IJCAI 2013, BeijingGoogle Scholar
  6. 6.
    Sharad N, Aayush M, Murty MN (2018) Fusing diversity in recommendations in heterogeneous information networks, WSDM 2018, Marina Del ReyGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and AutomationIndian Institute of ScienceBengaluruIndia

Personalised recommendations