Temporal Profiling for Opportunistic Partnership Recommendation

  • Authors:
  • Adriana S. Vivacqua;Carlos Eduardo Mello;Diogo K. Souza;João A. Avellar Menezes;Leandro C. Marques;Marcos S. Ferreira;Jano M. Souza

  • Affiliations:
  • Graduate School of Engineering, COPPE/UFRJ,;Graduate School of Engineering, COPPE/UFRJ,;Graduate School of Engineering, COPPE/UFRJ,;Graduate School of Engineering, COPPE/UFRJ,;Graduate School of Engineering, COPPE/UFRJ,;Graduate School of Engineering, COPPE/UFRJ,;Graduate School of Engineering, COPPE/UFRJ, and DCC-IM Dept. of Computer Science, Institute of Mathematics, Federal University of Rio de Janeiro, Brazil

  • Venue:
  • Computer Supported Cooperative Work in Design IV
  • Year:
  • 2008

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Abstract

Locating experts or work partners in large communities can sometimes be hard. The most common way of accomplishing this task is through recommendations from known acquaintances. This networked search for others who fit required profiles is a form of social navigation. However, needs, interests and expertise change rapidly, so time is an important factor in this type of situation, and recommendations need to be time-sensitive. This paper presents a peer-to-peer system for social network navigation, which builds user profiles through a temporal analysis of ongoing activities and matches these to find opportunities for collaboration.