Підходи До Зниження Природнього Шуму В Групових Рекомендаційних Системах

Abstract

This paper addresses the issue of the impact of noise on the effectiveness of group recommender systems (GRS). Noise in this context is defined as irrelevant or false information that can distort the recommendations displayed by the system. The article analyzes in detail the sources of noise in GRSs, including incorrectly entered data by users, data processing errors, and fluctuations in user preferences. Significant attention is paid to filtering strategies and algorithms that can be used to reduce the impact of noise, thus providing more accurate and useful recommendations for user groups.The paper proposes four approaches to reducing natural noise based on the assumption that there is local data related to the preferences of group members and global data related to the preferences of all users of the recommender system. One of the main aspects of the article is the discussion of different methodologies for aggregating group preferences, which allow to optimize information processing and increase the relevance of recommendations for all group members. The authors point out the importance of choosing an adequate aggregation strategy, as it affects the system's ability to minimize the impact of noise and take into account the dynamics and diversity of preferences within the group. The article also presents an experimental study that illustrates the application of the discussed methods in practice. Through the analysis of real data and modeling of GDS usage scenarios, the effectiveness of different techniques in the context of different types of groups and interaction scenarios is investigated. The final part of the paper emphasizes potential areas for further research in the field of optimizing group recommender systems, in particular, the development of more flexible and adaptive systems that can effectively cope with noise-related challenges. The authors call for greater attention to machine learning and artificial intelligence aspects that can help further develop and improve GRSs, increasing their accuracy and usefulness in real-world settings.