Research Article Open Access

The Exploration of Restaurant Recommender System

Tora Fahrudin1 and Nelsi Wisna1
  • 1 School of Applied Sciences, Telkom University, Indonesia

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 of Computer Science
Volume 18 No. 8, 2022, 784-791

DOI: https://doi.org/10.3844/jcssp.2022.784.791

Submitted On: 13 March 2022 Published On: 2 September 2022

How to Cite: Fahrudin, T. & Wisna, N. (2022). The Exploration of Restaurant Recommender System. Journal of Computer Science, 18(8), 784-791. https://doi.org/10.3844/jcssp.2022.784.791

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Keywords

  • Restaurant
  • Recommender System
  • Rating
  • Collaborative Filtering