Abstract:
[Purpose/significance] With the rapid development of academic social media, when users do interdisciplinary research or seek interdisciplinary cooperation, many scientific research cooperation starts from the acquaintance or attention in social media, so it is very meaningful to research on interdisciplinary user recommendation in academic social media. There are two main types of data in social media:media (represents content) and social (represents relationship). Therefore, this paper recommended interdisciplinary users integrating content and relations. [Method/process] After user modeling based on Vector Space Model, this paper calculated user specialization with user content information, measured user's interdisciplinary distance based on relational data, then gave recommendation results combined with PageRank value of user relationship network. [Result/conclusion] Taking the science blog as an example, an interdisciplinary user recommendation model in five fields of "Library and Information", "Computer", "News and Media", "Higher Education" and "Biology" been implemented, which has been tested by artificial experiments, and showed that the recommendation results can meet the recommendation requirements to some extent.