A Novel Hybrid Recommendation System for Riyadh Restaurants: Integrating Content-Based and Context-Aware Techniques

Recommendation systems are often used to deliver consumers a customised set of services. Their main purpose is to provide recommendations or concepts (such as restaurants, tourist attractions, medicines, movies, etc.) that effectively address consumer issues and may be used across several sectors. In today's environment, when there are so many eating alternatives, selecting the ideal restaurant to meet our interests may be difficult. Saudi Arabia's tourist industry has lately emerged as an important economic engine. It is becoming more difficult for customers to choose among the vast array of restaurants available in Riyadh due to the industry's fast expansion in recent years. This research presents a comprehensive recommendation system for Riyadh restaurants that integrates content-based filtering and context-aware location analysis to provide personalised suggestions. Utilising a dataset from Kaggle with information on 20,000 restaurants, the system employs a hybrid approach combining TF-IDF and cosine similarity for content-based filtering with geographic distance calculations for location-based recommendations. The hybrid model balances content relevance with geographic proximity, enhancing user satisfaction by delivering accurate and contextually relevant restaurant recommendations. The outcomes demonstrate an effectiveness of a hybrid method, outperforming individual techniques by generating more relevant and practical recommendations. The uniqueness of this study resides in the use of hybrid filtering, which has not before been used in comparable investigations. A more detailed and easily digestible list of suggested models is what this study report is aiming to provide. According to the findings and conclusion, the proposed strategy is effective in recommending restaurants in Riyadh.