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Multi-Objective Group Discovery on the Social Web

  • Behrooz Omidvar-Tehrani
  • Sihem Amer-Yahia
  • Pierre-Francois Dutot
  • Denis Trystram
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9851)

Abstract

We are interested in discovering user groups from collaborative rating datasets of the form \(\langle i, u, s\rangle \), where \(i \in \mathcal{I}\), \(u \in \mathcal{U}\), and s is the integer rating that user u has assigned to item i. Each user has a set of attributes that help find labeled groups such as young computer scientists in France and American female designers. We formalize the problem of finding user groups whose quality is optimized in multiple dimensions and show that it is NP-Complete. We develop \(\alpha \)-MOMRI, an \(\alpha \)-approximation algorithm, and h-MOMRI, a heuristic-based algorithm, for multi-objective optimization to find high quality groups. Our extensive experiments on real datasets from the social Web examine the performance of our algorithms and report cases where \(\alpha \)-MOMRI and h-MOMRI are useful.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Behrooz Omidvar-Tehrani
    • 1
  • Sihem Amer-Yahia
    • 2
  • Pierre-Francois Dutot
    • 2
  • Denis Trystram
    • 2
  1. 1.The Ohio State UniversityColumbusUSA
  2. 2.Univ. Grenoble Alps, CNRSGrenobleFrance

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