A New Grouping Method Based on Social Choice Strategies for Group Recommender System

  • Abinash PujahariEmail author
  • Vineet Padmanabhan
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)


Recommender System is a software or tool that helps users to select items or things according to their preferences. These are used in almost every web sites today. A lot of research is going on, how to produce efficient recommendations for individuals. Even more, today the recommendation of items/things are for a group of users where there is more than one user in a group and each user have their own preferences. Group Recommender System recommends items or things for a group of users based on their individual preferences. There are many social choice grouping strategies available. We proposed a new grouping algorithm which will first generate homogeneous groups and then generate recommendation of items for them. In this paper we followed the rule based approach to learn the user’s preferences. All the results of our approach is validated with the movie lens data set which is the bench mark data set for recommender system testing.


Recommender system Rule learning Predictive rule mining 


  1. 1.
    Basu, C., Hirsh, H., Cohen, W., et al.: Recommendation as classification: using social and content-based information in recommendation. In: AAAI, pp. 714–720. (1998)Google Scholar
  2. 2.
    Ricci, F., Rokach, L., Shapira, B.: Group recommender systems: combining individual models. Recommender Systems Handbook, pp. 677–702. Springer, Heidelberg (2011)Google Scholar
  3. 3.
    McCarthy, K., Salam, M., Coyle, L., McGinty, L., Smyth, B., Nixon, P.: Group recommender systems: a critiquing based approach. In: Proceedings of the 11th International Conference on Intelligent User Interfaces, pp. 267–269. ACM (2006)Google Scholar
  4. 4.
    Jameson, A.: More than the sum of its members: challenges for group recommender systems. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 48–54. ACM (2004)Google Scholar
  5. 5.
    Padmanabhan, V., Seemala, SK., Bhukya, WN.: A rule based approach to group recommender systems. In: Multi-Disciplinary Trends in Artificial Intelligence, pp. 26–37. Springer, Heidelberg (2011)Google Scholar
  6. 6.
    Yin, X., Han, J.: CPAR: Classification based on predictive association rules. In: SDM SIAM, vol. 3, pp. 369–376. (2003)Google Scholar
  7. 7.
    Zaier, Z., Godin, R., Faucher, L.: Evaluating recommender systems. In: Automated Solutions for Cross Media Content and Multi-channel Distribution, AXMEDIS’08 International Conference on IEEE, pp. 211–217. (2008)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Institute of Information TechnologySambalpur UniversityJyoti Vihar, BurlaIndia
  2. 2.School of Computer and Information SciencesUniversity of HyderabadHyderabadIndia

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