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Multi-Objective Group Discovery on the Social Web

Conference paper
First Online:
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.

Keywords

User Group Discovery Heuristic-based Algorithms Recording Rate Constrained Multi-objective Optimization Problem Movie Rating Website 
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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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.The Ohio State UniversityColumbusUSA
  2. 2.Univ. Grenoble Alps, CNRSGrenobleFrance

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