【摘要】 教参书保障与支持服务是高校图书馆对教学活动提供支持服务的重要任务之一。从馆藏图书中识别出高利用率教参书,是后续深入开展教参书保障支持和精准服务的基础和前提条件。基于机器学习方法特点和海量用户动态行为数据,本文首先从借阅人群、借阅时间、利用率三个方面选取了七个维度的核心特征指标对高利用率教参书进行特征指标建模。随后,基于四种主流、典型机器学习算法:支持向量机、决策树、随机森林以及XGBoost,进行了对比实验。实验表现最好的XGBoost算法的查准率(Precision)、查全率(Recall)和F1分数(F1-score)分别为0.849、0.906、0.876,取得了较好的识别结果。更多还原

【Abstract】 The support services related to reference books is one important task of university libraries. The prerequisite of carrying out that is to identify the frequently utilized reference books. Based on machine learning methods and massive dynamic data about user behaviors, this paper first constructs seven-dimensional feature sets about borrower types, borrowing time and utilization rate to establish the identification model of frequently utilized reference books. In the experiment session, Support Vector Machine, Decision Tree, Random Forest and XGBoost algorithm are selected for comparison. Among them, XGBoost algorithm yields the best experimental performance. To be specific, its precision, recall and F1-score are 0.849, 0.906, 0.876. In summary, we obtain good identification result in this paper.更多还原