CAJ | 학술논문

随着全球信息总量的爆炸式增长，信息超载问题无法避免且日趋严重化。个性化推荐系统是当前解决信息过载问题的有效技术。该文首先阐述了推荐系统概念定义及其三大组成模块，其次深入分析了个性化推荐算法，详细讨论了当前主流的四大类推荐算法：基于内容的推荐算法、协同过滤推荐算法、基于知识的推荐算法和混合的推荐算法，从多角度对各算法的优缺点进行对比，然后阐述了常用评价方法、评测指标及对测试标准进行分类，并且介绍了常用数据集，最后展望个性化推荐未来研究热点。
수착전구신식총량적폭작식증장，신식초재문제무법피면차일추엄중화。개성화추천계통시당전해결신식과재문제적유효기술。해문수선천술료추천계통개념정의급기삼대조성모괴，기차심입분석료개성화추천산법，상세토론료당전주류적사대유추천산법：기우내용적추천산법、협동과려추천산법、기우지식적추천산법화혼합적추천산법，종다각도대각산법적우결점진행대비，연후천술료상용평개방법、평측지표급대측시표준진행분류，병차개소료상용수거집，최후전망개성화추천미래연구열점。
As the explosive growth of global information ,the information overload is one of the most critical prob-lems and is becoming more and more serious .Recommendation systems are one of the powerful ways to solve the problem.The definition of recommendation system is first introduced .A comparison study is conducted based on the four main recommendation algorithms: content-based recommendation , collaborative filtering recommendation , knowledge-based recommendation and hybrid recommendation .The evaluation methods , evaluation metrics and recommendation benchmarked datasets are also presented .At last the difficulties and future directions of recommen-dation systems are given .