Personalized Restaurant Recommendations: A Hybrid Filtering Approach for Mobile Applications

Authors

  • Christopher Matthew Marvelio Universitas Multimedia Nusantara
  • Alexander Waworuntu Universitas Multimedia Nusantara

DOI:

https://doi.org/10.31937/ijnmt.v12i1.4248

Abstract

Selecting a restaurant that suits your taste can be a major challenge for consumers, especially given the vast array of online dining options. Traditional recommendation systems or simple filtering methods often fail to handle this complexity well. To address these limitations, we developed a mobile app-based restaurant recommendation platform that combines content-based filtering and collaborative filtering methods in a hybrid approach. The application was built using Expo, React Native, Express, and Flask technologies. The evaluation was conducted using the End-User Computing Satisfaction (EUCS) framework, and the results showed a very high user satisfaction rate of 93.9%. This result shows that the recommendation system we developed is effective in providing relevant suggestions and is well received by users.

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Published

2025-06-30

How to Cite

Marvelio, C. M., & Waworuntu, A. (2025). Personalized Restaurant Recommendations: A Hybrid Filtering Approach for Mobile Applications. IJNMT (International Journal of New Media Technology), 12(1), 61–72. https://doi.org/10.31937/ijnmt.v12i1.4248