Comparative Analysis of Algorithms of Recommender Systems

Information that is generated and stored on the Internet, every day increases, which is largely due to the continuing advances in computer technology. Recommender systems have proven to be valuable tools for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Many web services have begun to provide the user with personalized offers of goods, formed independently for each user based on his actions on the site. It allows business to increase loyalty and satisfaction of users, to increase the frequency of visits, to reduce churn, to increase users consumption and, as a result of all these, increase business income. However, they require a lot of money and time for successful implementation. Development of recommender systems is a multi-disciplinary effort, which involves experts from various fields and requires a lot of money and time. Nobody knows in advance which method will show better results in a particular task. Anyone who wants to introduce recommender systems into their business has to make a research to find the best method of implementing the system in the required field. The purpose of the study is to compare the methods of developing recommender systems at the theoretical level and the practical level in the field of prediction of film ratings.