Deep Learning Enabled Social Media Recommendation Based on User Comments

作     者：K.Saraswathi V.Mohanraj Y.Suresh J.Senthilkumar 

作者机构：Department of Computer Science and EngineeringGovernment College of EngineeringSalem636011India Department of Information TechnologySona College of TechnologySalem636005India 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第44卷第2期

页      面：1691-1702页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Recommendation systems(RS) social media recursive neural network-based trust recommender system(RNN-TRS) user reviews 

摘      要：Nowadays,review systems have been developed with social media Recommendation systems(RS).Although research on RS social media is increas-ing year by year,the comprehensive literature review and classification of this RS research is limited and needs to be *** previous method did notfind any user reviews within a time,so it gets poor accuracy and doesn’tfilter the irre-levant comments effi*** Recursive Neural Network-based Trust Recom-mender System(RNN-TRS)is proposed to overcome this method’s *** it is efficient to analyse the trust comment and remove the irrelevant sentence ***first step is to collect the data based on the transactional reviews of social *** second step is pre-processing using Imbalanced Col-laborative Filtering(ICF)to remove the null values from the *** the features from the pre-processing step using the Maximum Support Grade Scale(MSGS)to extract the maximum number of scaling features in the dataset and grade the weights(length,count,etc.).In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax acti-vation function for calculating the average weights of the ***,In the classification method,the Recursive Neural Network-based Trust Recommender System(RNN-TRS)for User reviews based on the Positive and negative scores is analysed by the *** simulation results improve the predicting accuracy and reduce time complexity better than previous methods.