Improved Collaborative Filtering Algorithm Based on Apriori Association Analysis
WANG Qi
WANG Xun
HUANG Shucheng
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.
Keywords：collaborative filteringassociation analysistimelinesssimilarityhybrid recommendation
Publication Date：2025-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 617-622,665 )