## 1 Introduction  
Saudi Arabia has become increasingly reliant on its tourism sector as an economic driver. The study in [1] explores the link between tourism and economic growth in Saudi Arabia and concludes that tourism has a significant impact on promoting economic growth in the country. The food service sector in Saudi Arabia is expected to experience significant growth, with a projected increase in value from USD 14.16 billion in 2022 to USD 30.47 billion by 2029, at a CAGR of 11.57% in the forecast period [2]. The restaurant industry in the capital city of Riyadh has experienced rapid growth in recent years [3,4], resulting in increased competition and making it challenging for customers to choose from the large number of restaurants available. Furthermore, the exponential growth of information and content on the Internet has made it increasingly challenging for users to find relevant search results [5] while simultaneously presenting a challenge for marketers to make their products and services stand out. To address this challenge, recommender systems can be used to provide personalized restaurant recommendations to customers based on their preferences. Recommender systems are commonly employed in electronic tourism to offer various services, such as suggesting trips and activities, presenting lists of points of interest (POIs)that match user preferences, including historical landmarks, restaurants, shopping areas, and more [6]. With the recent growth of the tourism and hospitality sector in Saudi Arabia, specifically in line with the Saudi Vision 2030, there is a growing need for such systems. However, to the best of our knowledge, there are currently no similar works that target Riyadh city. The development of an effective restaurant recommender system for Riyadh has several potential benefits, including improved user satisfaction, increased revenue for restaurants, and a more vibrant and competitive restaurant industry in Riyadh. Building upon the proven effectiveness of matrix factorization in providing personalized recommendations to users [7; 8; 9; 10], this paper incorporates matrix factorization approaches for restaurant recommendations in Riyadh. Specifically, we utilize three matrix factorization techniques, namely, non-negative matrix factorization (NMF), singular value decomposition (SVD), and optimized singular value decomposition (SVD++), to implement a collaborative filtering recommender system. Matrix factorization can handle large and sparse datasets, it can capture complex relationships between users and restaurants, and it can provide personalized recommendations to users [5; 6]. Additionally, we build a content-based filtering model that measures the similarity between different restaurant features, allowing us to make personalized recommendations based on a user's preferences. To the best of our knowledge, this is the first recommender system specifically designed for Riyadh restaurants. To support our research, we collected a comprehensive dataset of restaurants in Riyadh, with a total of 14,520 unique values, scraped from Foursquare.com. This dataset is unique and valuable because it includes a wide range of restaurant features and attributes, allowing for more accurate and relevant recommendations. The dataset is publicly available on Kaggle.com as the Riyadh restaurants ([[https://www.kaggle.com/datasets/fahd09/riyadh-restaurants-20k](https://www.kaggle.com/datasets/fahd09/riyadh-restaurants-20k)]([https://www.kaggle.com/datasets/fahd09/riyadh-restaurants-20k](https://www.kaggle.com/datasets/fahd09/riyadh-restaurants-20k))) dataset, enabling other researchers to replicate the experiments and build upon the work. To evaluate the performance of our system, we conducted experiments using this real-world dataset and compared our results to a popularity-based recommender system. Our evaluation metrics included root mean squared error (RMSE) and mean absolute error (MAE) to measure the accuracy of our recommendations.