Hybrid Recommender System Using Systolic Tree for Pattern Mining  

作　　者：S.Rajalakshmi K.R.Santha 

机构地区：[1]Department of Computer Science Engineering,Sri Venkateswara College of Engineering,Sriperumbudur,India [2]Department of Electrical and Electronics Engineering,Sri Venkateswara College of Engineering,Sriperumbudur,India

出　　处：《Computer Systems Science & Engineering》2023年第2期1251-1262,共12页计算机系统科学与工程（英文）

摘　　要：A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved.

关 键 词：Recommender systems hybrid recommender systems frequent pattern mining collaborativefiltering systolic tree river formation dynamics particle swarm optimization 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]