个性化推荐算法研究

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．

Survey of Personalized Recommendation Algorithms

As the explosive growth of global information，the information overload is one of the most critical problems 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 recommendation systems are given.