计算机与数字工程2024，Vol.52Issue(1)：156-161,6.DOI:10.3969/j.issn.1672-9722.2024.01.025
融合动态K近邻Slope_One的协同过滤推荐算法
Integrating Dynamic K-nearest Neighbor Slope_One into Collaborative Filtering Algorithm
摘要
Abstract
Data sparse is a problem of traditional collaborative filtering algorithm,which will cause the algorithm to be insuffi-cient.The Slope_One algorithm is simple and efficient,and can predict the user's rating of an item.Therefore,this paper proposes a collaborative filtering recommendation algorithm combining dynamic K-nearest neighbor Slope_One to improve the accuracy of the algorithm.First,the improved cosine similarity formula is used to calculate the user similarity,K neighbor users are screened to cal-culate the average score deviation,the Slope_One algorithm is used to predict the corresponding user score,and effectively the score is filled into data matrix,and then the item-based collaborative filtering algorithm is used for recommendation.关键词
协同过滤/K近邻/Slope_One算法/数据稀疏Key words
collaborative filtering/K nearest neighbors/Slope_One algorithm/data sparse分类
信息技术与安全科学引用本文复制引用
李灵慧,王逊,王云沼,黄树成..融合动态K近邻Slope_One的协同过滤推荐算法[J].计算机与数字工程,2024,52(1):156-161,6.基金项目
国家自然科学基金项目"基于鲁棒表现建模的目标跟踪方法研究"(编号:61772244)资助. （编号:61772244）
