TY  - JOUR
AU  - Fahrudin, Tora 
AU  - Wisna, Nelsi 
PY  - 2022
TI  - The Exploration of Restaurant Recommender System
JF  - Journal of Computer Science
VL  - 18
IS  - 8
DO  - 10.3844/jcssp.2022.784.791
UR  - https://thescipub.com/abstract/jcssp.2022.784.791
AB  - 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.