摘要
提出基于电子资源行为数据的TF-IDF文献推荐方法,旨在提高图书馆的资源使用效率,实现文献推荐服务个性化、精准化。首先,使用基于相同专业背景用户下载、浏览文献的摘要构成语料库;其次,对语料库中的摘要进行分词,计算分词后词条的TF-IDF值;然后,选取该专业高影响期刊文献为待推荐文献;最后,计算待推荐文献和语料库各词条的TF-IDF值的余弦相似度,并将相似度高的文献推送给用户。实证结果表明,一方面该推荐方法为用户推荐的文献精准性较高,另一方面,为学者们在有效推荐论文研究方面提供了新的思路。
The TF-IDF literature recommendation method based on the behavior data of electronic resources is proposed,aiming to improve the efficiency of using library point resources and realizing personalized and accurate literature recommendation services.Firstly,a corpus is formed by using the abstracts downloaded and browsed by users with the same professional background.Secondly,the abstracts in the corpus are divided into words and the TF-IDF values of the divided words are calculated.Then,the high-impact journal literature of the profession is selected as the literature to be recommended.Finally,the cosine similarity between the literature to be recommended and the TF-IDF values of each entry in the corpus is calculated,and the literature with high similarity is sent to the user.The empirical results show that,on one hand,the recommendation method is more accurate for users with similar academic research backgrounds,and,on the other hand,it provides new ideas for scholars to effectively recommend papers for research.
作者
刁羽
薛红
Diao Yu;Xue Hong(Sichuan University of Science and Engineering Library)
出处
《图书馆杂志》
CSSCI
北大核心
2022年第12期45-54,共10页
Library Journal
基金
四川省文化和旅游厅2020-2022年图书情报学与文献学规划项目“基于用户访问电子资源行为数据的个性化学科服务实证研究——以创文图书馆电子资源综合管理与利用系统为例”(项目编号:WHTTSXM[2020]07)研究成果之一。
关键词
电子资源
行为数据
TF-IDF
文献推荐
Electronic resource
Behavior data
TF-IDF
Literature recommendation
