## 2 Background and Related Works
### Related Works  
Recommender systems have become increasingly prevalent in a variety of domains, including e-commerce, music, movies, and restaurants. In the tourism and hospitality sector, several studies have explored different approaches to recommending restaurants to users. One such study [21] introduced a machine learning-based recommender system. Their dataset, collected from TripAdvisor.com, included hotel information, users' reviews, and ratings of the hotels. The authors used amenities as features and also considered user ratings for each hotel as a feature. They further detected the sentiment of the reviews using natural language processing (NLP) techniques. The authors implemented five different NLP techniques, including probabilistic neural network (PNN), back propagation neural network (BPN), support vector machine (SVM), and linear discriminate analysis (LDA), and achieved an accuracy of 92.45%. Moreover, the researchers in [22] proposed a location-aware personalized traveler assistance (LAPTA) system that integrates user preferences and GPS to generate personalized and location-aware recommendations for tourists. The system separates data obtained from Google locations into name and category tags and fetches keywords from the user's input based on their past research behavior. LAPTA uses the K-Nearest algorithm to match name and category tags with the user's input to generate personalized suggestions. Experimental results showed that LAPTA could provide more reliable and accurate recommendations compared to reviewed recommendation applications, making it a valuable tool for tourists seeking personalized recommendations. In [23], the authors aimed to explore the relationship between reviewers' evaluations and overall satisfaction. The authors collected their dataset from Yelp, a restaurant review website, and were interested in overall numerical ratings and textual comments. They implemented a Latent Aspect Rating Analysis (LARA) with modifications using NLP, machine learning, and regression analysis to predict the overall numerical rating of an entity. The resulting F1-scores for each class were presented for each city, with a range of F-scores of 0.80-0.88 for rating, 0.69-0.87 for overall satisfaction, and 0.73-0.87 for price. [PERSON] et al. [24] developed a restaurant recommender system using a user-based collaborative filtering approach. They used a dataset provided by Zomato, consisting of 50,998 ratings, 86 restaurants, and 593 users. The system aimed to provide recommendations based on the similarity of preferences for the target user and all existing users by calculating the weighted coefficient for the similarity between users and similarity of user attributes. The performance was evaluated using the mean absolute error (MAE) metric through two scenarios: user-based collaborative and user-based collaborative with user attributes. The performance showed 1.4 MAE for the first scenario and 2.1 for the second scenario, indicating that the system performs better without user attributes. [PERSON] [25] proposes a recommender system for food-delivery applications based on the number of orders. The system uses the nearest-neighbor technique to calculate a client's preferred restaurants and analyze other clients with similar buying patterns. The paper also proposes a performance metric based on real restaurant sales. The proposed system was tested on a real dataset and was found to give an average of 7.7 options out of 187 available. The system was compared to other state-of-the-art recommendation techniques and was found to perform better. The study also found that the majority of the works are either based on surveys or rely on data from social networks like TripAdvisor, Zomato, Yield, and others. In addition, [PERSON] [26] proposes a context-aware food recommender system which provides personalized food recommendations based on a user's profile, physiological signals, and sensing environmental information around the dining table in real time.