• Ilham Huseyinov
• Tolga Hamitovalı

Recommendation Systems are designed to provide a personalized product or service to the user. Its purpose is to predict the future actions of users based on their past behavior and make suggestions accordingly. Recent studies have proven that Deep Learning-based collaborative filtering method has a high success rate. However, there is no study on the implementation of this method in restaurant recommendation systems. The goal of this study is to fill this gap. For this purpose, different models were designed using different restaurant datasets and a deep learning-based collaborative filtering algorithm. Using the evaluation criteria of Restaurant Recommendation Systems, the models developed in this study were compared with the results of other studies. The comparison results are presented graphically. As a result, the hyperparameters of the most optimal model based on the Deep Learning-based collaborative filtering algorithm were found.

Collaborative Filtering, Deep Learning, Neural Collaborative Filtering, Matrix Factorization, Multi-layer Perceptron, NeuMF, Layer, Inner Product

"Developing Restaurant Recommendation System with Neural Collaborative Filtering Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 8, page no.a103-a108, August-2021, Available :http://www.jetir.org/papers/JETIR2108015.pdf

"Developing Restaurant Recommendation System with Neural Collaborative Filtering Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 8, page no. ppa103-a108, August-2021, Available at : http://www.jetir.org/papers/JETIR2108015.pdf