Open Access

ARTICLE

Profile and Rating Similarity Analysis for Recommendation Systems Using Deep Learning

Lakshmi Palaniappan^1,*, K. Selvaraj²

1 Department of Computer Science and Engineering, Veerammal Engineering College, K.Singarakottai, Dindigul, 624708, India
2 Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, 624622, India

* Corresponding Author: Lakshmi Palaniappan. Email:

Computer Systems Science and Engineering 2022, 41(3), 903-917. https://doi.org/10.32604/csse.2022.020670

Received 02 June 2021; Accepted 03 July 2021; Issue published 10 November 2021

Abstract

Recommendation systems are going to be an integral part of any E-Business in near future. As 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 him. In general, the recommendations to a user are made based on similarity that exists between the intended user and the other users. This similarity can be calculated either based on the similarity between the user profiles or the similarity between the ratings made by the users. First phase of this work concentrates on experimentally analyzing both these models and get a deep insight of these models. With the lessons learned from the insights, second phase of the work concentrates on developing a deep learning model. The model does not depend on the other user's profile or rating made by them. The model is tested with a small restaurant dataset and the model can predict whether a user likes the restaurant or not. The model is trained with different users and their rating. The 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 restaurants. The model is deployed in a cloud environment in order to extend it to be more realistic product in future. Result evaluated with dataset, it achieves 74.6% is accurate prediction of results, where as existing techniques achieves only 64%.

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