虚拟学术社区中融合用户动态兴趣与社交关系的学者推荐研究

Purpose/Significance] Considering the dynamic changes of users interests and social relationships, this paper proposes a scholar recommendation model integrating users dynamic interests and social relationships. [Method/Process] Firstly, using the periodical literature of different disciplines as the classified corpus, the discipline domain of scholars blog posts was distinguished based on the labeled LDA model. Then KNN algorithm was used to classify blogs by discipline. At the same time, the change rate of subject interests was used to improve the time factor, and the dynamic interest similarity of scholars was calculated. The PageRank of scholars was calculated by using the quantitative relationship of links between scholars, and the global trust level was calculated by combining the PageRank and time value of blogs sent by scholars. Time weight was introduced into scholars comments and recommendation interaction behaviors to calculate scholars interactive trust level. The dynamic social trust level of scholars was obtained by integrating the global trust level and interactive trust level. Finally, the similarity of interest and trust were combined to recommend scholars. [Result/Conclusion] The scholar recommendation model integrating users dynamic interests and social relationships in the virtual academic community can effectively improve the quality of scholar recommendation from the perspectives of dynamic interests and dynamic social relationships.