| 英文标题 | Study of listwise learning-to-rank recommendation method based on heterogeneous network analysis in scientific social network |
| 作者英文名 | Yue Feng, Wang Hanru, Zhang Xinyue, Wang Gang |
| 机构英文名 | a.School of Computer Science & Information Engineering,b.School of Management,Hefei University of Technology,Hefei 230009,China |
| 英文摘要 | In view of the fact that the existing recommendation methods for academic papers cannot make full use of the hete-rogeneous relations between entities in scientific social network, and most of them focus on the accuracy of predicted ratings, ignoring users' preference order, this paper proposed a heterogeneous network analysis based listwise learning-to-rank method. Firstly, it employed heterogeneous network analysis to fully explore the complex relations between entities in the scientific social network. On this basis, it integrated the information obtained from heterogeneous network analysis into the listwise method to optimize the ranking of the papers, which finally got the recommendation list of papers to researchers. This paper conducted experiments on the dataset of ScholarMate(one of the prevalent scientific social networks). And the experimental results show that the proposed method performs better than traditional recommendation methods, which illustrates the effectiveness of the proposed method. |
| 英文关键词 | scientific social network; paper recommendation; heterogeneous network; listwise learning-to-rank |