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Profile and Rating similarity Analysis for Recommendation Systems Using Deep Learning

Computer Systems Science & Engineering 2022年第6期41卷 903-917页

作者： Lakshmi Palaniappan K.Selvaraj Department of Computer Science and Engineering Veerammal Engineering CollegeK.SingarakottaiDindigul624708India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India

Recommendation systems are going to be an integral part of any E-Business in near *** in any other E-business,recommendation systems also play a key role in the travel business where the user has to be recommended with a restaurant that best suits *** general,the recommendations to a user are made based on similarity that exists between the intended user and the other *** similarity can be calculated either based on the similarity between the user profiles or the similarity between the ratings made by the *** phase of this work concentrates on experimentally analyzing both these models and get a deep insight of these *** the lessons learned from the insights,second phase of the work concentrates on developing a deep learning *** model does not depend on the other user's profile or rating made by *** model is tested with a small restaurant dataset and the model can predict whether a user likes the restaurant or *** model is trained with different users and their *** system learns from it and in order to predict whether a new user likes or not a restaurant that he/she has not visited earlier,all the data the trained model needed is the rating made by the same user for different *** model is deployed in a cloud environment in order to extend it to be more realistic product in *** evaluated with dataset,it achieves 74.6%is accurate prediction of results,where as existing techniques achieves only 64%.

关键词： Deep learning restricted boltzman machine profile based similarity rating based similarity item based similarity

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