Formalizing the Stages of Mammographic Examinations in Designinga Medical Decision Support System

Povoroznyuk, Anatoly; Shehna, Khaled; Povoroznyuk, Oksana

doi:10.15276/hait.04.2020.6

Cited by 1 publication

(1 citation statement)

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“…There have been many different models of information filtering that have evolved since 1992, beginning with the study that was conducted by Loeb et al [15]. One of these models is called content-based filtering, and it is used to propose things to consumers that have qualities that are comparable to those that already exist in their collection.…”

Section: Content-based Filteringmentioning

confidence: 99%

Recommender systems: models, challenges and opportunities

Hodovychenko,

Gorbatenko

2023

HAIT

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The purpose of this study is to provide a comprehensive overview of the latest developments in the field of recommender systems. In order to provide an overview of the current state of affairs in this sector and highlight the latest developments in recommender systems, the research papers available in this area were analyzed. The place of recommender systems in the modern world was defined, their relevance and role in people's daily lives in the modern information environment were highlighted. The advantages of recommender systems and their main properties are considered. In order to formally define the concept of recommender systems, a general scheme of recommender systems was provided and a formal task was formulated. A review of different types of recommender systems is carried out. It has been determined that personalized recommender systems can be divided into content filtering-based systems, collaborative filtering-based systems, and hybrid recommender systems. For each type of system, the author defines them and reviews the latest relevant research papers on a particular type of recommender system. The challenges faced by modern recommender systems are separately considered. It is determined that such challenges include the issue of robustness of recommender systems (the ability of the system to withstand various attacks), the issue of data bias (a set of various data factors that lead to a decrease in the effectiveness of the recommender system), and the issue of fairness, which is related to discrimination against users of recommender systems. Overall, this study not only provides a comprehensive explanation of recommender systems, but also provides information to a large number of researchers interested in recommender systems. This goal was achieved by analyzing a wide range of technologies and trends in the service sector, which are areas where recommender systems are used.

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Section: Content-based Filteringmentioning

confidence: 99%