计算机与数字工程2025，Vol.53Issue(3)：617-622,665,7.DOI:10.3969/j.issn.1672-9722.2025.03.001
基于Apriori关联分析的协同过滤改进算法
Improved Collaborative Filtering Algorithm Based on Apriori Association Analysis
摘要
Abstract
Collaborative filtering techniques are widely used in personalized recommendation systems.However,their limita-tions in handling data sparsity often lead to insufficient accuracy in recommendation results.This paper proposes an improved collab-orative filtering algorithm by introducing association rule mining for association analysis.Firstly,effective strong association rules are obtained through Apriori association analysis to construct a recommendation score calculation method,which is then used for rat-ing prediction.At the same time,considering the timeliness issues of traditional collaborative filtering algorithms in recommendation systems,penalty terms and time factors are introduced to optimize the original similarity measurement algorithm,reducing the error caused by the randomness of similarity calculations.Finally,a hybrid recommendation result is generated by integrating two differ-ent recommendation strategies.Experimental results show that,compared with classical collaborative filtering methods,the pro-posed improved algorithm significantly alleviates the data sparsity problem and enhances prediction accuracy,thereby improving the performance of the recommendation system.关键词
协同过滤/关联分析/时效性/相似度/混合推荐Key words
collaborative filtering/association analysis/timeliness/similarity/hybrid recommendation分类
计算机与自动化引用本文复制引用
王琦,王逊,黄树成..基于Apriori关联分析的协同过滤改进算法[J].计算机与数字工程,2025,53(3):617-622,665,7.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）
