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Group Recommender Systems-Evolutionary Approach Based on Consensus with Ties

  • Ritu Meena
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 701)

Abstract

The issue regarding aggregation of multiple rankings into one consensus ranking is an interesting research subject in a ubiquitous scenario that includes a group of users. For minimizing the fitness value of Kendall tau distance (KtD), the well-known optimal aggregation method of Kemeny is used to generate an aggregated list from the input lists. A primary goal of our work is to recommend a list of items or permutation that can effectively handle the problem of full ranking with ties using consensus (FRWT-WC). Additionally, in real applications, most of the studies have focused on without ties. However, the rankings to be aggregated may not be permutations where elements have multiple choices ordered set, but they may have ties where some elements are placed at the same position. In this work, in order to handle problem of FRWT in GRS using consensus measure function, KtD are used as fitness function. Experimental result are presents that our proposed GRS based on Consensus for FRWT (GRS-FRWT-WC) outperforms well-knows baseline GRS techniques. In this work, we design and evolve an innovative method to solve the problem of ties in GRS based on consensus and results show that efficiency of group does not certainly reduce in which the group has similar-minded user.

Keywords

Group recommender systems Rank aggregation Genetic algorithm Kendall tau distance Consensus 

Notes

Acknowledgements

The work presented here has been supported partly by DST-PURSE and partly the RGNF-SRF for the scholar.

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Meena, R., Bharadwaj, K.K.: Group recommender system based on rank aggregation–an evolutionary approach. In: Proceedings of the International Conference on Mining Intelligence and Knowledge Exploration (MIKE), LNCS 8284, Springer, pp. 663–676 (2013)CrossRefGoogle Scholar
  3. 3.
    Salamo, M., McCarthy, K., Smyth, B.: Generating recommendations for consensus negotiation in group personalization services. Pers. Ubiquit. Comput. 16(5), 597–610 (2012)CrossRefGoogle Scholar
  4. 4.
    Baltrunas, L., Makcinskas, T., Ricci, F.: Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the 4th ACM Conference on Recommender Systems (RecSys 2010), pp. 119–126Google Scholar
  5. 5.
    Anand, D., Bharadwaj, K.K.: Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities. Exp. Syst. Appl. Elsevier (2010)Google Scholar
  6. 6.
    Sascha, H., Rösch, S., Beckmann, C., Gross, T.: Informing the design of group recommender systems. CHI Extend. Abst. (2012)Google Scholar
  7. 7.
    Baskin Jacob, P., Krishnamurthi, S.: Preference Aggregation in Group Recommender Systems for Committee Decision-Making. (RecSys 2009), pp. 337–340Google Scholar
  8. 8.
    Bharadwaj, K.K., Al-Shamri, M.Y.H.: Fuzzy-Genetic approach to recommender systems based on a novel hybrid user model. Exp. Syst. Appl. Elsevier 35, 1386–1399 (2007)Google Scholar
  9. 9.
    Cantador, I., Castells, P.: Group recommender systems: new perspectives in the social web. In: J.J. Pazos Arias, A. Fernández Vilas, R.P. Díaz Redondo (Eds.): Recommender Systems for the Social Web. Springer, Intelligent Systems Reference Library, Vol. 32, ISBN: 978–3-642-25693-6 (2012)Google Scholar
  10. 10.
    Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the Tenth International Conference on the World Wide Web, pp. 613–622, Hong Kong (2001)Google Scholar
  11. 11.
    Nguyen, H.D., Yoshihara, I., Yasunaga, M.: Modified edge recombination operators of genetic algorithm for the travelling salesman problem. In: Proceedings of the IEEE International Confeence on Industrial Electronics, Control, and Instrumentation (2000)Google Scholar
  12. 12.
    García, J.M., Tapia, Moral, M. J., del, Martínez, M.A., Herrera-Viedma, E.: A consensus model for group decision making problems with linguistic interval fuzzy preference relations. Expert Syst. Appl. 39(11), 10022–10030 (2012)Google Scholar
  13. 13.
    Ioannidis, S., Muthukrishnan, S., Yan, J.: A Consensus-focused group recommender system. CoRR abs/1312.7076 (2013)Google Scholar
  14. 14.
    Brancotte, B., Yang, B., Blin, G., Boulakia, S.C., Denise, A., Hamel, S.: Rank aggregation with ties: experiments and analysis. PVLDB 8(11), 1202–1213 (2015)Google Scholar
  15. 15.
    Fagin, R., Kumar, R., Mahdian, M., Sivakumar, D., Vee, E.: Comparing and Aggregating Rankings with Ties. Pods 47–58 (2004)Google Scholar
  16. 16.
    Lawrence, D.: Schedule optimization using genetic algorithms. In: Handbook of Genetic Algorithms, ed. Van Nostr, Reinhold, New York (1991)Google Scholar
  17. 17.
    Melanie, M.: An Introduction to Genetic Algorithms. MIT Press, ISBN 978–0-262-63185-3, pp. I–VIII, 1-208 (1998)Google Scholar
  18. 18.
    Salamo, M., McCarthy, K., Smyth, B.: Generating recommendations for consensus negotiation in group personalization services. Pers. Ubiquit. Comput. 16(5), 597–610 (2012)CrossRefGoogle Scholar
  19. 19.
    Chuan-Kang, T.: Improving edge recombination through alternate inheritance and greedy manner. Evo COP 2004, 210–219 (2004)zbMATHGoogle Scholar
  20. 20.
    Garcia, I., Pajares, S., Sebastia, L., Onaindia, E.: Preference elicitation techniques for group recommender systems. Informat. Sci. 189, 155–175 (2012)CrossRefGoogle Scholar
  21. 21.
    Onaindia, E., García, I., Sebastia, L.: A negotiation approach for group recommendation. In: Proceedings of the International Conference on Artificial Intelligence (ICAI-2009), CSREA Press, pp. 919–925Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Computer & Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

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