Deep Learning Enabled Social Media Recommendation Based on User Comments  

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

机构地区：[1]Department of Computer Science and Engineering,Government College of Engineering,Salem,636011,India [2]Department of Information Technology,Sona College of Technology,Salem,636005,India

出　　处：《Computer Systems Science & Engineering》2023年第2期1691-1702,共12页计算机系统科学与工程（英文）

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 improved.The previous method did notfind any user reviews within a time,so it gets poor accuracy and doesn’tfilter the irre-levant comments efficiently.The Recursive Neural Network-based Trust Recom-mender System(RNN-TRS)is proposed to overcome this method’s problem.So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately.Thefirst step is to collect the data based on the transactional reviews of social media.The second step is pre-processing using Imbalanced Col-laborative Filtering(ICF)to remove the null values from the dataset.Extract 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 features.Finally,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 system.The simulation results improve the predicting accuracy and reduce time complexity better than previous methods.

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

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]