@article {10.3844/jcssp.2022.784.791,
article_type = {journal},
title = {The Exploration of Restaurant Recommender System},
author = {Fahrudin, Tora and Wisna, Nelsi},
volume = {18},
number = {8},
year = {2022},
month = {Sep},
pages = {784-791},
doi = {10.3844/jcssp.2022.784.791},
url = {https://thescipub.com/abstract/jcssp.2022.784.791},
abstract = {The exploitation of Recommender Systems (RS) isstill a challenge, hence it is important to explore the three correlatedattributes, such as restaurant, food, and service ratings. Therefore, thisstudy provides an in-depth review of these attribute ratings using theCollaborative Filtering (CF) technique. Experiments were performed with k-NN,SVD, Slope One, and Co-Clustering algorithms, while RMSE, MSE, MAE, and FCPwere used as evaluation metrics. The results showed that the service restaurantrating predictions produced the best average MSE and RMSE accuracy in 5 and10-fold cross-validation. Furthermore, the best hyperparameter of algorithmsusing Grid Search was achieved in restaurant rating prediction. In conclusion,SVD surpasses other algorithms in MSE and RMSE for all scenarios.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}