Unraveling the Relationship between Co-Authorship and Research Interest

Co-authorship in scientific research is increasing in the past decades. There are lots of researches focusing on the pattern of co-authorship by using social network analysis. However, most of them merely concentrated on the properties of graphs or networks rather than take the contribution of authors to publications and the semantic information of publications into consideration. In this paper, we employ a contribution index to weight word vectors generated from publications so as to represent authors’ research interest, and try to explore the relationship between research interest and co-authorship. Result of curve estimation indicates that research interest couldn’t be employed to predict co-authorship. Therefore, graph-based researcher recommendation needs further examination

A Graph-based profile similarity calculation method for collaborative information retrieval

International audienceAs the volume of information augments, the importance of the Information Retrieval (IR) increases. Collaborative Information Retrieval (CIR) is one of the popular social-based IR approaches. A CIR system registers the previous user interactions to response to the subsequent user queries more efficiently. But CIR suffers from the personalization problem because the goals and the characteristics of two users may be different; so when they send the same query to a CIR system, they may be interested in two different lists of documents. We have developed a personalized CIR system, called PERCIRS, to solve this problem. Selecting an efficient method to calculate the similarity between the users’ profiles is a key factor for enhancing PERCIRS’s efficiency. In this paper, we propose a new graph-based method for user profile similarity calculation. Finally, by introducing an evaluation method, we will show that this new method is more efficient than the previous methods