Restaurant Recommendation System in Malaysia Using Machine Learning Approach

• Published in: Frontiers in Artificial Intelligence and Applications
• Main Author: Idalisa N.; Hazhar M.H.M.; Muslim N.; Albashah N.L.S.
• Format: Conference paper
• Language: English
• Published: IOS Press BV 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217178785&doi=10.3233%2fFAIA241357&partnerID=40&md5=ebf7cbe73d3e9c5f4607886dfdac529c

People frequently struggle to make decisions when faced with a wider range of possibilities, especially when selecting a dining restaurant. To address this issue, a recommendation system can assist by analyzing user preferences and previous dining experiences to offer personalized suggestions. This research aims to develop a restaurant recommendation system for Malaysian customers using a machine-learning approach. The study focuses on Non-negative Matrix Factorization (NMF), Probability Matrix Factorization (PMF), Principal Component Analysis (PCA), and Singular Value Decomposition (SVD) approaches. Based on an analysis of 2,496 datasets gathered from the TripAdvisor platform, the findings revealed that the SVD method outperformed other approaches, achieving a Root Mean Square Error of 0.1166. This result positions SVD as the most suitable method for developing a restaurant recommendation system. The proposed system features a user-friendly interface built with Streamlit, allowing users to select their location and receive top restaurant suggestions. Additionally, users can view recommendations based on their past dining experiences. The system retrieves all reviews for the selected restaurants and converts them into a Term Frequency-Inverse Document Frequency (TF-IDF) matrix. Cosine similarity is then employed to measure the relevance of review content using the computed TF-IDF. Finally, the system also recommends similar restaurants based on the user's chosen options, enhancing the overall dining experience. © 2024 The Authors.