Hybrid Recommendation Algorithm for Personalization of Customer Experience

With the widespread use of the internet, a massive volume of data is created on the network every second, while the user needs information that is relevant to his particular search. The processing of such large amounts of data is a difficult task. An information retrieval system that can analyze this enormous data is necessary to provide the user with the exact information he or she requires. A recommendation system is a type of needed technology that obtains information in order to enhance users' access to information and, as a result, recommends items that are relevant to his clearly stated behavior and preferences. The recommendation system examines a large data collection and focuses on providing the user with reliable content recommendations. There are a variety of recommendation systems in use today, including Netflix, YouTube, Tinder, and Amazon, to name a few. This article examines the numerous types of recommendation systems, as well as their varied difficulties and solutions, as well as application cases of correctly employed recommendation engines and their possible advantages.