## 2 Background and Related Works
### Related Works
The proposed system aims to provide appropriate food recommendations for each individual's health at the table in real time, making it suitable for well-being care applications. Furthermore, Visit Saudi [27] represents the official tourism platform for the Kingdom of Saudi Arabia. It serves as a comprehensive resource for tourists seeking information about popular destinations, activities, events, and travel tips for visiting Saudi Arabia. One of its features is \"Plan Your Trip\", which recommends cultural events, local cuisine, and other relevant topics related to Saudi Arabia. Recent studies have noted the incorporation of deep learning techniques such as neural networks and convolutional neural networks into restaurant recommender systems. These techniques can capture complex patterns and relationships in the data and can handle large and sparse datasets. For example, Reference [28] proposes a restaurant food recommender system that uses optical character recognition (OCR) technology and a transformer-based deep learning model to recommend food dishes to users based on their nutritional needs. The system is able to rank the food dishes in terms of the input search key (e.g., calorie, protein level) and can also rank unseen food dishes. The proposed system was evaluated using a self-constructed menu dataset, known as MenuRank dataset, and achieved promising results with accuracy ranging from 77.2% to 99.5%. The evaluation metrics used were normalized discounted cumulative gain (NDCG), cross entropy loss (CEL), and accuracy (ACC). In addition, the work in [29] proposes an explainable food recommender system that uses a deep learning-based image clustering method to recommend food based on visual content and justify the recommendations. The system also introduces a new similarity score based on a tendency measure that quantifies the extent to which the user community prefers a given food category, and a rule-based explainability to enhance transparency and interpretability of the recommendation outcome. The experiments on a crawled dataset show that the proposed method enhances recommendation quality compared to other existing methodologies for food recommendation. Context-aware recommender systems that consider the context in which the recommendation is made, such as the time of day, weather, and user location, have also been gaining attention. These systems can provide more relevant and personalized recommendations to users. For example, a recent study [30] proposed a social network mobile application for tourism using a hybrid recommender system that incorporates user location and time of day to recommend restaurants to users. Their approach achieved high accuracy and improved the relevance of the recommendations. In addition, the authors of [31] proposed a serendipity-oriented personalized trip recommendation (SOTR) algorithm to address the challenges in trip recommendation. The proposed algorithm considers similarity between points of interest (POIs) and category differences among consecutive POIs and uses serendipity to recommend a trip with high user satisfaction. The algorithm was evaluated using real-life and Foursquare datasets, and the results showed that it outperforms various recommendation methods by satisfying user interests in the trip. Furthermore, the authors of [32] proposed a method to extract user food preferences from their online comments about restaurants. The method is based on natural language processing techniques for processing the text of user comments and extracting the desired food names. The semantic similarity approach has been exploited to conceptually cluster food names. The clustering results with the Wu-Palmer method shows higher precision. To obtain the opinion of the user regarding each food item on the menu, sentiment analysis is performed to indicate whether their opinion is positive or negative. The proposed system suggests surrounding restaurants to users with an accuracy of 92.8%.