【摘要】 现如今推荐算法已得到广泛应用,但大多数推荐算法均存在各自的局限性。针对这一问题,提出一种基于Boosting框架的推荐系统架构,以多种基本推荐算法为基础,集成一个强推荐系统。将基于Boosting的推荐系统,在MovieLens 100K中进行测试。测试与分析结果表明,该系统测试结果显示Precision达到39.44%,比原来提高8.63%。因此,集成的推荐系统能够有效提升推荐效果,为用户提供良好的用户体验。更多还原

【Abstract】 Nowadays,the recommendation algorithms have been widely used in various fields,but most of them have their own limitations. A recommendation system architecture based on Boosting framework is proposed to solve this problem,by which a strong recommendation system is integrated on the basis of a variety of basic recommendation algorithms. The Boosting-based recommendation system is tested in MovieLens 100 K. The testing and analysis results show that the precision of the system reaches 39.44%,which is 8.63% higher than that of the original system. Therefore,the integrated recommendation system can effectively improve the recommendation effect and provide users with a good experience.更多还原