## 2 Background and Related Works
### Related Works
Matrix factorization is another popular approach in the recent literature for restaurant recommendations. Several studies have explored the use of matrix factorization for improving the accuracy and diversity of restaurant recommendations. For example, the authors of [33] proposed a hybrid recommender system that considered visual features in addition to text-based information to predict favorite restaurants. The recommender system was implemented using three techniques: factorization machine (FM), matrix factorization (MF), and Bayesian personalized ranking matrix factorization (BPRMF). The dataset was collected from a restaurant social platform that consists of user information, restaurant information, and blog articles written by users about their visited restaurants. The authors used blog articles and extracted 2118 keywords related to restaurants and food as text-based information and images embedded in the articles as visual information. The performance was evaluated based on the three selected techniques (FM, MF, and BPRMF) enhanced by content-based features. The results showed that the enhanced BPRMF and enhanced FM performed better than other techniques. Another recent approach in the literature is graph-based recommender systems. These systems represent users and items as nodes in a graph and use graph-based algorithms to make recommendations. For example, Reference [34] focuses on developing an application named \"Travel Best\" that extracts check-ins from users' Facebook accounts to generate trend-based suggestions for location and restaurant recommendations. The application utilizes shortest path, longest path, and most traveled route algorithms to suggest locations within a city based on Facebook checked-in data. Additionally, the study utilizes opinion mining to provide relevant restaurant ratings based on food quality, service, and ambiance, unlike overall ratings usually provided by other applications. The results are visualized using a graph database. Overall, the proposed approach provides personalized location and restaurant recommendations based on users' behavior, which can help users plan their trips more effectively. Another recent study by [35] introduces a new dataset named CADT (city-attraction dataset for travel) to explore the use of conversational recommender systems (CRS) in tourism attraction and city recommendation. The authors propose a new framework named RUI (representation, understanding, interaction) to make travel recommendations using a path-seeking process on the given knowledge graph. Both automatic and human evaluations were conducted, which demonstrated the effectiveness of the RUI framework. The study presents the first knowledge graph-based CRS for city and tourism attraction recommendations in the travel domain. Our study builds upon these recent studies by proposing a collaborative-based recommender system using a matrix factorization algorithm for Riyadh restaurants. The system showed that the quality of the recommendations overcome the baselines. Our system has the potential to improve the overall dining experience for customers and increase revenue for restaurants in Riyadh.