摘要
在协同过滤算法中,准确地找到相似用户是关键。稀疏数据问题常导致推荐性能低下,且推荐结果大都大量存在热门物品。文章基于物品(User-IIF)和用户(Item-IUF)的热门惩罚后的相似度计算方法,定义稀疏度的计算方式,并进行稀疏度加权来改善稀疏性问题,分别提出了两种融合后的协同过滤算法。改进后的两种融合算法更有效地利用了用户信息,提高了算法对降低热门物品干扰的效果,增加了推荐系统发现长尾物品的能力。实验采用MovieLens公开数据集,进行多次实验,实验结果表明改进后的两种融合算法在推荐结果的准确率和覆盖率上有显著提升,表明融合后的推荐算法在寻找相似用户或物品时表现得更高效。
In collaborative filtering algorithms,In the collaborative filtering algorithm,accurately finding similar users is the key.Sparse data problems often result in poor recommendation performance.In addition,most of the current recommendation systems ignore the recommendation for long tail items,and most of the recommended results are hot items.In this paper,we use the calculation method of similarity,the object-based(User-IIF)and user-based(Item-IUF),and define how sparsity is calculated,weighting sparsity to deal with data sparsity problem,and finally propose two fusion collaborative filtering algorithms.The improved two fusion algorithms can make more efficient use of users’information,and improve the effect of the algorithm on reducing the interference of popular items,increasing the ability of the recommendation system to find long tail items.In this paper,the experiment used the MovieLens public data set and several experiments have been carried out.The experimental results show that the improved two fusion algorithms have significantly improved the accuracy and coverage of the recommended results,indicating that the proposed recommendation algorithm is more efficient when searching for similar users or items.
作者
邓亚文
罗可
DENG Ya-wen;LUO ke(School of Computer and Communication Engineering,Changsha University of Science and Technology,Changsha 410114,China;Hunan Provincial key Laboratory of Intelligent Processing of Big Data on Transportation,Changsha University of Science and Technology,Changsha 410114,China)
出处
《电脑与信息技术》
2019年第1期6-10,33,共6页
Computer and Information Technology
基金
国家自然科学基金项目(NO.11671125
NO.71371065)资助
关键词
基于用户
相似度计算
基于物品
协同过滤
稀疏性问题
user-based
similar calculations
item-based
collaborative filtering
sparse problem
