摘要
采用协同过滤方式的传统推荐系统具有一定实用性,但也存在未考虑用户个性喜好的问题。为提高推荐精度,特别针对用户个性化特点和需求,提出了采用改进相似度计算和回归分析方法对协同过滤推荐进行系统优化。实验结果表明,优化算法可明显改善系统的推荐效果,并加强基于协同过滤推荐的有效性。
The traditional recommendation systems generally use collaborative filtering but merely consider the personality and individual preference. To enhance the recommendation quality,this paper proposed an approach to optimize the existing collaborative filtering recommendation system by adding the similarity calculation and regression analysis. The experimental results show the algorithm can significantly improve the quality of recommendation system and the collaborative filtering efficiency.
出处
《杭州电子科技大学学报（自然科学版）》
2015年第3期56-59,共4页
Journal of Hangzhou Dianzi University：Natural Sciences
基金
浙江省自然科学基金资助项目(LY13F010011)
关键词
个性化
推荐系统
回归分析
personalization
recommendation system
regression analysis
