基于多维计量权重的学术论文推荐模型  

An Academic Paper Recommendation Model Based on Multidimensional Metric Weights

作　　者：迟玉琢 李金灿 Chi Yuzhuo;Li Jincan

机构地区：[1]黑龙江大学信息管理学院,黑龙江哈尔滨150006

出　　处：《山东图书馆学刊》2024年第5期39-48,共10页The Library Journal of Shandong

摘　　要：本文针对基于内容的论文推荐系统推荐论文在质量与时效性等方面存在的不足,提出了一种基于多维计量权重的学术论文推荐模型。结合内容个性化推荐的技术方法和文献计量学,从论文的期刊影响力、作者贡献度、文献老化指数等多个维度对论文推荐过程进行优化,并采用层次分析法确定各维度指标的权重,最终生成个性化的论文推荐列表。模型实证效果较好,可为优化信息服务方式、减轻研究人员文献筛选负担提供新思路。The paper introduces an academic paper recommendation model based on multidimensional metric weights to address the deficiencies in quality and timeliness of content-based paper recommendation systems.By in-tegrating the techniques of personalized content recommendation and bibliometrics,the paper recommendation process is optimized across multiple dimensions including journal influence,author contribution,and literature aging index.The Analytic Hierarchy Process is employed to ascertain the weights of indicators within each dimension,culminating in the production of a personalized paper recommendation list.The model demonstrates effective empirical perform-ance,offering fresh insights for enhancing information service strategies and alleviating the literature screening bur-dens on researchers.

关 键 词：学术论文推荐 内容推荐系统 文献计量学 多维指标 

分 类 号：G250.252[文化科学—图书馆学]