Modeling collaborative knowledge of publishing activities for research recommendation

Authors:
Tin Huynh;Kiem Hoang
Affiliations:
University of Information Technology, HCMC, Vietnam;University of Information Technology, HCMC, Vietnam
Venue:
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Year:
2012

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Abstract

We have applied social network analysis (SNA) approach for our current researches that relate to recommender systems in the field of scientific research. One of the challenges for SNA based methods is how to identify and quantify relationships of actors in a specified social community. In this context, how we can extract and organize a social structure from a collection of scientific articles. In order to do so, we proposed and developed a collaborative knowledge model of researchers from their publishing activities. The collaborative knowledge model (CKM) forms a collaborative network that is used to represent, qualify collaborative relationships. The proposed model is based on the combination of graph theory and probability theory. The model consists of three key components such as CoNet (a scientific collaborative network), M (measures) and R (rules). The model aims to support recommendations for researchers such as research paper recommendation, collaboration recommendation, expert recommendation, and publication venue recommendation that we have been working on.