Research on PMF Model Based on BP Neural Network Ensemble Learning Bagging and Fuzzy Clustering

Zhengjin Zhang,Baojin Shi,Siwei Wei,Jiabao Jiang,Guilin Huang,Baohua Liang,Yong Zhang,Muhammad Javaid

doi:10.1155/2021/9985894

Abstract

Probability matrix factorization model can be used to solve the problem of high-dimensional sparsity of user and rating data in the recommender systems. However, most of the existing methods use the user to model the item rating, ignoring the relationship between the user and the item, so the accuracy of user-item rating prediction is still low. Therefore, this paper proposes a probabilistic matrix factorization model based on BP neural network ensemble learning, bagging, and fuzzy clustering. Firstly, the membership function of fuzzy clustering and the selection of cluster center are used to calculate the user-item rating matrix; secondly, BP neural network trains the user-item scoring matrix after clustering, further improving the accuracy of scoring prediction; finally, the bagging method in ensemble learning is introduced, which takes the number of user-item scores as the base learner, trains the base learner through BP neural network, and finally obtains the score prediction through the voting results, which improves the stability of the model. Compared with the existing PMF models, the root mean square error of the PMF model after fuzzy clustering is increased by 9.27% and 3.95%, and the average absolute error is increased by 21.14% and 1.11%, respectively; then, the performance of the first mock exam is introduced. The root mean square error of the ensemble method is increased by 4.02% and 0.42%, respectively, compared with the existing single model. Finally, the weights of BP neural network training based learner are introduced to improve the accuracy of the model, which also verifies the universality of the model.

Highlights

In recent years, matrix factorization technology, with good scalability and high recommendation accuracy, has developed rapidly [1]
A probability matrix factorization model by fusing the ensemble learning bagging method based on BP neural network and fuzzy clustering is proposed. e main work is as follows: (1) e scoring matrix of users and items is calculated by using the membership function of fuzzy matrix and the selection of cluster center, which is more accurate than the traditional probability matrix method and can better construct the scoring matrix of users and items
We mainly test our hypothesis through several groups of experiments: FCM clustering methods are applied to the probabilistic matrix factorization (PMF) model from different aspects to achieve the purpose of prediction accuracy

Summary

Introduction

Matrix factorization technology, with good scalability and high recommendation accuracy, has developed rapidly [1]. Koren put forward a new SVD++ model by combining the matrix factorization model with the domain-based recommendation method [2]. Fang et al [5] integrated the recommendation methods based on user similarity, used different similarity measures to generate different recommendation models, and weighted sum to get Complexity the final prediction score, which improved the prediction accuracy of the model. A probability matrix factorization model by fusing the ensemble learning bagging method based on BP neural network and fuzzy clustering is proposed. (2) e bagging method in ensemble learning is proposed to generate different training sets by selfsampling, and ensemble learning is introduced into this model, increasing the parallelism and improving the accuracy and stability of scoring prediction

System Model

Probability Matrix Factorization Model with Fuzzy Clustering

Experiments

Findings

Conclusions