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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
Salamo, M., McCarthy, K., Smyth, B.: Generating recommendations for consensus negotiation in group personalization services. Pers. Ubiquit. Comput. 16(5), 597–610 (2012)
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. 119–126
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)
Sascha, H., Rösch, S., Beckmann, C., Gross, T.: Informing the design of group recommender systems. CHI Extend. Abst. (2012)
Baskin Jacob, P., Krishnamurthi, S.: Preference Aggregation in Group Recommender Systems for Committee Decision-Making. (RecSys 2009), pp. 337–340
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)
Cantador, I., Castells, P.: Group recommender systems: new perspectives in the social web. In: J.J. Pazos Arias, A. Fernández Vilas, R.P. DÃaz Redondo (Eds.): Recommender Systems for the Social Web. Springer, Intelligent Systems Reference Library, Vol. 32, ISBN: 978–3-642-25693-6 (2012)
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)
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)
GarcÃa, J.M., Tapia, Moral, M. J., del, MartÃnez, 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)
Ioannidis, S., Muthukrishnan, S., Yan, J.: A Consensus-focused group recommender system. CoRR abs/1312.7076 (2013)
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)
Fagin, R., Kumar, R., Mahdian, M., Sivakumar, D., Vee, E.: Comparing and Aggregating Rankings with Ties. Pods 47–58 (2004)
Lawrence, D.: Schedule optimization using genetic algorithms. In: Handbook of Genetic Algorithms, ed. Van Nostr, Reinhold, New York (1991)
Melanie, M.: An Introduction to Genetic Algorithms. MIT Press, ISBN 978–0-262-63185-3, pp. I–VIII, 1-208 (1998)
Salamo, M., McCarthy, K., Smyth, B.: Generating recommendations for consensus negotiation in group personalization services. Pers. Ubiquit. Comput. 16(5), 597–610 (2012)
Chuan-Kang, T.: Improving edge recombination through alternate inheritance and greedy manner. Evo COP 2004, 210–219 (2004)
Garcia, I., Pajares, S., Sebastia, L., Onaindia, E.: Preference elicitation techniques for group recommender systems. Informat. Sci. 189, 155–175 (2012)
Onaindia, E., GarcÃa, I., Sebastia, L.: A negotiation approach for group recommendation. In: Proceedings of the International Conference on Artificial Intelligence (ICAI-2009), CSREA Press, pp. 919–925
Acknowledgements
The work presented here has been supported partly by DST-PURSE and partly the RGNF-SRF for the scholar.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Meena, R. (2018). Group Recommender Systems-Evolutionary Approach Based on Consensus with Ties. In: Satapathy, S., Tavares, J., Bhateja, V., Mohanty, J. (eds) Information and Decision Sciences. Advances in Intelligent Systems and Computing, vol 701. Springer, Singapore. https://doi.org/10.1007/978-981-10-7563-6_37
Download citation
DOI: https://doi.org/10.1007/978-981-10-7563-6_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7562-9
Online ISBN: 978-981-10-7563-6
eBook Packages: EngineeringEngineering (R0)