关键词: 学术社交网络, 团队推荐, 非负矩阵分解, 多维度

Abstract: Traditional research methods have been greatly changed by academic social networks (ASNs), this paper explores the area of personalized team recommendation for scholars in ASNs, proposes a novel model named multi-faceted team recommendation (MFTR). MFTR recommends latent scientific research teams with similar research interest for users not only by computing the similarities of users and scientific research teams based on their eigenvector which is abstracted by methods of projected gradient non-negative matrix factorization, but also combining the relationship of friends and active scientific research teams. MFTR is conducted by comprehensive experiments on real world datasets from SCHOLAT that is an academic social site. The results show that the model can efficiently improve the quality of recommendation and abate the problem of cold start.

Key words: academic social networks, team recommendation, non-negative matrix factorization, multi-faceted