Presenting a personalized web recommender system based on hierarchical clustering and deep autoencoding

The amount of information published on the web has made the use of recommender systems inevitable. Web recommender systems provide users with accurate and relevant suggestions based on their interests and tastes. These recommendations can help users quickly access the information they need and reduce search time. In this article, a hybrid recommender system based on the combination of fuzzy sequential clustering and deep self-encrypting network based on user profile information and ranking of websites by users is presented. In this recommender system, users are first clustered according to the similarity of their opinions. Then the new ranking for users is predicted according to the fuzzy membership function. Finally, the information available in the user profile and the new rating of the users to each website is used as the input of the presented deep encoder network in order to predict the rating of the websites by the users. Finally, after finding similar users, it will recommend visiting and personalizing the web page of serious users based on the favorite websites of similar users. According to the layers of in-depth training and completion of the training process in the middle layer, the proposed method has been able to outperform other classification methods in terms of statistical accuracy of about 42% and the ratio of successful recommendations to useful recommendations of about 4% and accuracy The detection of similar users has improved by about 20%.