A Personalized Web Recommendation System Based on a Weighted user Behavior Profile by Applying Extended Learning Method

The huge amount of information has forced researchers to find a solution to face this fundamental problem called data overload. Recommender systems try to suggest the required information to the user by examining the user's preferences directly or based on the behavior of other similar users in a way that best matches the user's needs. Meanwhile, the use of textual information hidden in the user's biography or comments can be very useful. Declarative systems try to find similarities by examining each word in users' comments with the comments of other users, this is if different meanings for a word are ignored. In this way, the use of auto-encoder networks in order to check the semantic relationship of words in a sentence with respect to the opinions of other users can overcome this challenge. In this article, a personalization approach is presented based on the recommendation system in social networks using the combination of collaborative filter and deep auto-encoder networks. In proposed recommendation system, the information in the user profile and user comments to each website is used as the input of the presented combined deep auto-encoder network and the collaborative filter method in order to find similar users accurately and predict the website's rating by users. Finally, after finding similar users, it provides recommendations to visit and personalize the web page of serious users based on the favorite websites of similar users. Due to the convolutional layers of proposed deep auto-encoder network, the training process in the middle layer has performed on semantic relationship of words in a sentence to find similar comments and users. This method implemented on two standard datasets titled TripAdvisor and Yelp. The proposed method has improved in terms of statistical accuracy of about 39%, the ratio of successful recommendations to useful recommendations of about 6%, and the accuracy of recognizing similar users is about 18% from other classification methods.