Exploiting Result Diversification Methods for Feature Selection in Learning to Rank

  • Kaweh Djafari Naini
  • Ismail Sengor Altingovde
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)


In this paper, we adopt various greedy result diversification strategies to the problem of feature selection for learning to rank. Our experimental evaluations using several standard datasets reveal that such diversification methods are quite effective in identifying the feature subsets in comparison to the baselines from the literature.


Feature Selection Feature Selection Method Relevance Score Feature Selection Problem Modern Portfolio Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Carbonell, J.G., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proc. of SIGIR 1998 (1998)Google Scholar
  2. 2.
    Chelaru, S.V., Orellana-Rodriguez, C., Altingovde, I.S.: How useful is social feedback for learning to rank youtube videos? WWW Journal, 1–29 (in press), doi:10.1007/s11280-013-0258-9Google Scholar
  3. 3.
    Cunningham, P., Carney, J.: Diversity versus quality in classification ensembles based on feature selection. In: Lopez de Mantaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 109–116. Springer, Heidelberg (2000)Google Scholar
  4. 4.
    Dang, V., Croft, W.B.: Feature selection for document ranking using best first search and coordinate ascent. In: Proc. SIGIR 2010 Workshop on Feature Generation and Selection for Information Retrieval (2010)Google Scholar
  5. 5.
    Geng, X., Liu, T.-Y., Qin, T., Li, H.: Feature selection for ranking. In: Proc. of SIGIR 2007, pp. 407–414 (2007)Google Scholar
  6. 6.
    Gollapudi, S., Sharma, A.: An axiomatic approach for result diversification. In: Proc. of WWW 2009, pp. 381–390 (2009)Google Scholar
  7. 7.
    Herbrich, R., Graepel, T., Obermayer, K.: Large margin rank boundaries for ordinal regression. In: Advances in Large Margin Classifiers, pp. 115–132 (2000)Google Scholar
  8. 8.
    Peng, H., Long, F., Ding, C.H.Q.: Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  9. 9.
    Rafiei, D., Bharat, K., Shukla, A.: Diversifying web search results. In: Proc. of WWW 2010, pp. 781–790 (2010)Google Scholar
  10. 10.
    Santos, R.L.T., Castells, P., Altingovde, I.S., Can, F.: Diversity and novelty in information retrieval. In: Proc. of SIGIR 2013, p. 1130 (2013)Google Scholar
  11. 11.
    Vieira, M.R., Razente, H.L., Barioni, M.C.N., Hadjieleftheriou, M., Srivastava, D., Train Jr., C., Tsotras, V.J.: On query result diversification. In: Proc. of ICDE 2011, pp. 1163–1174 (2011)Google Scholar
  12. 12.
    Wang, J., Zhu, J.: Portfolio theory of information retrieval. In: Proc. of SIGIR, pp. 115–122 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kaweh Djafari Naini
    • 1
  • Ismail Sengor Altingovde
    • 2
  1. 1.L3S Research CenterLeibniz University HannoverHannoverGermany
  2. 2.Middle East Technical UniversityAnkaraTurkey

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