Presenting a Personalized Web Recommender System Based on Sequential Clustering and Deep Auto-Encoder
Subject Areas : electrical and computer engineering
mastooreh moeini
1
,
Ali Broumandnia
2
*
,
Mona Moradi
3
1 - Dept. of Software Eng., South Tehran Branch, Islamic Azad University, Tehran Iran
2 - Dept. of Software Eng., South Tehran Branch, Islamic Azad University, Tehran Iran
3 - Dept. of Software Eng., Central Tehran Branch, Islamic Azad University, Tehran Iran South Tehran Branch
Keywords: Recommender system, user profile, auto-encoder networks, collaborative filter.,
Abstract :
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 recommendations based on their interests and tastes. These recommendations can help users quickly access the information they need and reduce search time. In this paper, a hybrid recommender system based on the combination of fuzzy sequential clustering and deep Auto-encoder network based on user profile information and ranking of websites by users is presented.
In this recommender system, users are first sequentially 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 in the user profile and the new rating of users to each website is used as the input of the provided deep Auto-encoder network in order to predict the ranking of websites by users. Finally, after finding similar users, It provides recommendations to visit and personalize the web page of new users based on the favorite websites of similar users. The proposed method has improved compared to the following classification methods due to the layers of deep learning and completion of the learning process in the middle layer: In terms of statistical accuracy, about 42%, and the ratio of successful recommendations to useful recommendations is about 4%, and the accuracy of recognizing similar users is about 20%.
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