Open AccessResearch on Application of Improved Association Rules Mining Algorithm in Personalized Recommendation
Author(s) -
Jie Gao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1744/3/032111
Subject(s) - association rule learning , apriori algorithm , computer science , data mining , process (computing) , algorithm , value (mathematics) , recommender system , a priori and a posteriori , tree (set theory) , information retrieval , machine learning , mathematics , mathematical analysis , philosophy , epistemology , operating system
Traditional association rule mining does not consider the importance of each item, so the actual process lacks certain pertinence. Based on the New-Apriori algorithm and the Fp-growth algorithm idea, this paper proposes an improved association rule algorithm based on Fp-tree, Constructs the general process of personalized recommendation of association rules. And uses the Web log file to use the frequency of web pages being selected by users as the weight value, and realizes the algorithm of the personalized recommendation system. The experimental results show that the algorithm has high accuracy and effectiveness.