Aggregating Author Profiles from Multiple Publisher Networks to Build a List of Potential Collaborators

Recommender systems have roots in numerous fields, and their use is widespread in the modern world. The scientific community is striving to enhance the quality of life by breaking innovative barriers and developing solutions that had never previously been considered. In an ideal world, an individual researcher would participate in various fields of research and make cumulative impactful contributions to benefit society. However, in reality, this goal is difficult to attain without a team of collaborators. Collaboration refers to the information of partnerships that bring uniquely talented researchers together around a common idea. However, efforts to seek such co-authors not only are challenging but also occasionally yield no significant results. In this paper, we propose a recommender system to aggregate author information from multiple publisher networks. It evaluates the trustworthiness of the author recommendations based on the impact of the authors' contributions and the recency and popularity of their work as well as the correlations among these factors. On this basis, the system generates a list of prospective collaborators who might be of interest to a given researcher.