DETERMINE RECOMMENDATION SYSTEMS TO SEARCH FOR BOOKS BY PREFERENCES OF WEB USERS
Abstract
Currently, the question of state, formation and development of the information source interaction system, the scientific interaction and users' requests in certain fields of activity remains relevant under the conditions of the development of the use of Internet services. Recommendation systems are one of the types of artificial intelligence technologies for predicting parameters and capabilities. Due to the rapid increase in data on the Internet, it is becoming more difficult to find something really useful. And the recommendations offered by the service itself may not always correspond to the user's preferences. The relevance of the topic is to develop a personal recommendation system for searching books, which will not only reduce time and amount of unnecessary information, but also meet the user's preferences based on the analysis of their assessments and be able to provide the necessary information at the right time. All this makes resources based on referral mechanisms attractive to the user. Such a system of recommendations will be of interest to producers and sellers of books, because it is an opportunity to provide personal recommendations to customers according to their preferences. The paper considers algorithms for providing recommender systems (collaborative and content filtering systems) and their disadvantages. Combinations of these algorithms using a hybrid algorithm are also described. It is proposed to use a method that combines several hybrids in one system and consists of two elements: switching and feature strengthening. This made it possible to avoid problems arising from the use of each of the algorithms separately. A literature web application was developed using Python using the Django and Bootstrap frameworks, as well as SQLite databases, and a system of recommendations was implemented to provide the most accurate suggestion. During the testing of the developed software, the work of the literature service was checked, which calculates personal recommendations for users using the method of hybrid filtering. The recommendation system was tested successfully and showed high efficiency.
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