9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

Research Article

Profile Sharing Recommendation System for Enterprise Collaboration

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  • @INPROCEEDINGS{10.4108/icst.collaboratecom.2013.254076,
        author={Shivangi Agarwal and Krishna Dhara and Venkatesh Krishnaswamy},
        title={Profile Sharing Recommendation System for Enterprise Collaboration},
        proceedings={9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing},
        publisher={ICST},
        proceedings_a={COLLABORATECOM},
        year={2013},
        month={11},
        keywords={enterprise collaboration contextual collaboration user profiles collaboration relationship strength},
        doi={10.4108/icst.collaboratecom.2013.254076}
    }
    
  • Shivangi Agarwal
    Krishna Dhara
    Venkatesh Krishnaswamy
    Year: 2013
    Profile Sharing Recommendation System for Enterprise Collaboration
    COLLABORATECOM
    IEEE
    DOI: 10.4108/icst.collaboratecom.2013.254076
Shivangi Agarwal1, Krishna Dhara2,*, Venkatesh Krishnaswamy2
  • 1: Indian Institute of Technology
  • 2: Avaya Labs Research
*Contact email: dhara@avaya.com

Abstract

Collaboration among enterprise users is often enhanced by their prior history that may include summary of the topics discussed, people involved, documents shared, and other such profile information. In enterprises, unlike in consumer collaboration, sharing this profile information could be extremely valuable and would further enhance the effectiveness of users in a collaboration session. For instance, a sales team member interacting with a potential customer over a period of time can share the profile or contextual information he or she gathered with a new member of the team. Sharing such profile information would jump start the collaboration of the new member and enhance the effectiveness of the collaboration.
In this paper, we propose a session-based graph algorithm that recommends profile sharing possibilities to enterprise users based on the history of their collaboration strengths with persons interacting with them. We present results from implementing our algorithm on collaboration relationships that are computed on a deployed real-world enterprise server with approximately one million user-person relationships over a period of nine months.