RFM segmentasyonu ile bir hibrit öneri sistem uygulamasi

A recommendation system is a type of information filtering system established to suggest items or content to users based on their preferences and behavior by using data analysis techniques. The system is designed to predict what users might like based on their past interactions with the system or other users like them. To build a recommendation system, commonly used data analysis techniques are collaborative filtering, content-based filtering, and hybrid filtering. Collaborative filtering analyzes the past behavior of users and the items they have interacted with to identify patterns and similarities between users. Content-based filtering uses similar items, such as type or keywords, that the items have their own characteristics. Hybrid filtering combines these two approaches to provide more accurate recommendations. To implement a recommendation system, data is collected from various sources such as user ratings, user behavior, user demographics, and item characteristics. This data is then analyzed to identify patterns and relationships between users and items, and the results are used to suggest personalized recommendations to users. Overall, recommendation systems are a powerful tool for businesses to increase user engagement and improve customer satisfaction by providing personalized and relevant recommendations to users. Many online service providers use a recommendation system to assist their customers' decision- making by generating recommendations. Accordingly, this thesis proposes a new recommendation system for tourism customers to make online reservations for hotels with the features they need, saving customers time and increasing the impact of personalized hotel recommendations. This new system combined collaborative and content-based filtering approaches and created a new hybrid recommendation system. Two datasets containing customer information and hotel features were analyzed by Recency, Frequency, Monetary (RFM) method in order to identify customers according to their purchasing nature. The main idea of the recommendation system is to establish correlations between users and products and make the decision to choose the most suitable product or information for a particular user. As a result of the exponential growth of online data, this vast amount of information for use in the tourism industry can be leveraged by decision-makers to make purchasing decisions. Filtering, prioritizing, and beneficially presenting relevant information reduces this overload. There are following three main ways that recommendation systems can generate a recommendation list for a user; content-based, collaborative-based, and hybrid approaches. This thesis describes each category and its techniques in detail. RFM Analysis is used to identify customer segments by measuring customers' purchasing habits. It is the process of labeling customers by determining the Recency, Frequency, and Monetary values of their purchases and ranking them on a scoring model. Scoring is based on how recently they bought (Recency), how often they bought (Frequency), and purchase size (Monetary). Experimental results show that the accuracy of behavior analysis using Manhattan distance- based hybrid filtering is greatly improved compared to collaborative and content-based algorithms.