Collaborator recommendation in interdisciplinary computer science using degrees of collaborative forces, temporal evolution of research interest, and comparative seniority status

Currently, the research in computer science has been exponentially expanded beyond its own fields into the other research fields such as medical science, business, and social science in forms of collaborative researches. This collaborative researches stimulate a new recommending algorithm for determining a potential research collaborator under the interdisciplinary environment. Unlike other research fields, the research problems in computer science can be transformed to other known and solvable problems. In this paper, a new hybrid algorithm based on dynamic collaboration over time was proposed for recommending an appropriate collaborator. Besides considering only three basic factors concerning social proximity, friendship, and complementarity skill as employed by others’, three new additional factors related to research interest, up-to-date publication data, and seniority of researcher are involved in our analysis. A set of new measures for all six recommending factors were proposed. The experiments were conducted with real bibliographic data within six continuous years of publication and over six topics in computer science. Our results were significantly higher than the results of the other methods at 90% confidence level.