CAJ | 학술논문

信息超载和语言障碍影响我国科研人员对外文科技文献的有效获取，如何提高获取效率成为亟待解决的问题。个性化推荐能很好地处理信息超载现象，但当前国内外相关研究都基于单一语种进行，多语种环境下的推荐研究非常缺乏。本文提出网络环境和海量数据背景下的科技文献跨语言推荐模型，并论证用户兴趣特征抽取、语言转换和混合推荐等步骤。利用Web日志挖掘技术，分析基于多种信息行为的整合分析方法抽取用户兴趣特征，以分类表作为参考体系建立用户兴趣表示模型，在用户一特征词转化为用户一类目矩阵的基础上开展推荐研究。图3。参考文献17。
신식초재화어언장애영향아국과연인원대외문과기문헌적유효획취，여하제고획취효솔성위극대해결적문제。개성화추천능흔호지처리신식초재현상，단당전국내외상관연구도기우단일어충진행，다어충배경하적추천연구비상결핍。본문제출망락배경화해량수거배경하적과기문헌과어언추천모형，병론증용호흥취특정추취、어언전환화혼합추천등보취。이용Web일지알굴기술，분석기우다충신식행위적정합분석방법추취용호흥취특정，이분류표작위삼고체계건립용호흥취표시모형，재용호일특정사전화위용호일류목구진적기출상개전추천연구。도3。삼고문헌17。
Information overload and language barrier seriously affect the efficiency of acquiring academic literatures in foreign language and how to help users obtain their targeted literatures becomes an urgent problem for digital libraries. It is reported that personalized recommendation systems can well deal with information overload problem, but most of the current researches are based on single language and seldom discusses the methods of recommending academic literatures under a multilingual environment. The paper proposes a framework of cross-language recommendation system of academic articles, and its relevant modules are described in details, including extraction of users＇ interest feature, language translation and hybrid recommendation etc. This research is expected to expand the research content of personalized recommendation systems and provide technical solutions for improving the efficiency of obtaining academic articles in other languages. 3 figs. 18 refs.