面向科研跟踪推送的个性化知识服务模型

Personal Knowledge Recommendation Model for Scientific Pushing Sustainably

科研跟踪推送是大数据时代情报服务机构对科研工作的一项重要支持,鉴于已有方法直接应用于科研跟踪推送的局限性,设计面向科研跟踪推送的个性化服务模型。模型根据与科研人员相关的网页判断其总体研究兴趣,利用其发表的文献提取兴趣特征词,通过高频兴趣特征词生成兴趣检索式,对学术数据库进行检索获取推送文献,并将科研人员的特征信息和经过索引的推送文献信息存入相应数据库。最后,用一个示例演示了模型的工作原理。

Pushing sustainably to the scientists is an important task of information institution. For the limitation of exiting methods, this paper designs a personal knowledge recommendation model to provide scientific push sustainably. It first judges the scientists＇ interest by related webpages, and then extracts characteristic words from the scientists＇ papers. After constructing retrieval terms by high frequency characteristic words, it searches the academic databases, and indexes the results in order to store them in the database. At last, it demonstrates the work of the model through an example.