Scientific reports of Bukhara State University


Background. In this article, we look at the key advances in collaborative filtering recommender systems, focusing on the evolution from research focused solely on algorithms to research focused on the broad set of issues surrounding user experience with the recommender. The Internet provides a huge amount of heterogeneous information, and its number is increasing rapidly every year. Related to this is the concept of information overload, when a person is exposed to an excessive amount of information and is not able to effectively process and use information. Recommender systems are trying to eliminate this problem. However, the difficulty of evaluating and selecting relevant information is constantly increasing. Therefore, new recommendation systems using various evaluation strategies are constantly emerging. There are also studies that demonstrate the effectiveness of their use in solving problems with information retrieval and recommendations. Unlike traditional models (such as collaborative filtering), deep learning provides a better understanding of user requirements, item characteristics, and the historical interaction between them. Earlier methods use models such as matrix factorization, SVD (Singular Value decomposition), to recommend them. These systems only work with user ratings. Gradually, recommendation systems were created that also used available information about items. The recommender system filters out a piece of information based on user behavior or interest. The recommendation system can predict the interest of the user, as well as predict whether the user will prefer a particular product or not. For both users and service providers, a recommendation system is beneficial as well as effective in increasing the sales of many products. This article explores many of the recommender systems methods. The large amounts of unfiltered information returned by an internet query calls for filters able to validate and rank the available options. Recommender systems are a software tool designed to qualify the options available and make suggestions that align with the user’s requirements and expectations. It also reviews various filtering techniques like collaborative, content based, and hybrid. Method. In the article explains the basic approaches of the Recommendation Systems from several perspectives. In terms of input data, output data and various approaches that are used in these systems. Result. Recommender systems are used to estimate user preferences for items that users have not yet seen. Based on the output, we then divide recommendation systems with rating prediction, top-n item prediction, and classification. Conclusion. In this article, the recommended systems have been analyzed both in terms of internal representation and in terms of the methods that these traditional systems use. The rating prediction system seeks to fill in as many missing elements as possible in the matrix containing the ratings that the user has assigned to individual elements in the past. The result of the top-n system is an estimated list of elements of length n. The classification system focuses on classifying candidate items into the correct categories for recommendations.

First Page


Last Page





1. Bohnert F., Schmidt D.F., Zukerman I. Spatial Processes for Recommender Systems. [Online; navštíveno 29.12.2017].

2. Lu J., Wu D., Mao M. aj.: Recommender System Application Development s: A Survey 2019

3. Sarwar B., Karypis G., Konstan J. aj.: Item - based collaborative filtering recommendation algorithms. 2018

4. Resnick P., Iacovou N., Suchak M. aj.: GroupLens: an open architecture for collaborative filtering of netnews. 2018

5. Shambour Q., Lu J. hybrid trust - enhanced collaborative filtering recommendation approach for personalized government - to - business e - services, International Journal of Intelligent Systems. 2017

6. Madadipouya K., Chelliah S. A Literature Review on Recommender Systems Algorithms, Techniques and Evaluations. BRAIN: Broad Research in Artificial Intelligence and Neuroscience, ročník 8, č. 2, July 2017.

7. Lops P., de Gemmis M., Semeraro G. Content-based Recommender Systems: State of the Art and Trends. In: Ricci F., Rokach L., Shapira B., Kantor P. (eds) Recommender Systems Handbook. Springer, Boston, MA, 2011, ISBN 978-0-387-85819-7.

8. Lisa Wenige, Johannes Ruhland, Retrieval by recommendation: using LOD technologies to improve digital library search, © Springer Verlag GmbH Germany 2017

9. Mingdan Si, Qingshan Li, Shilling attacks against collaborative recommender systems: a review, Artificial Intelligence Review (2020) 53:291–319

10. Hui Li, Yan Gu, Saroj Koul, A Review of Digital Library Book Recommendation Models, 2015

11. Joseph A. Konstan, John Riedl, Recommender systems: from algorithms to user experience, © Springer Science+Business Media B.V. 2012

12. Emmanouil Vozalis, Konstantinos G. Margaritis, Analysis of Recommended Systems Algorithms, 2014

Included in

Life Sciences Commons



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.