PERSONALIZATION OF VISUAL CONTENT OF INTERACTIVE ART IN AUGMENTED REALITY BASED ON INDIVIDUAL USER PREFERENCES

Аndrii Kuliahin
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Abstract

Topicality. In connection with the development of AR technologies and their use in interactive art, there is a growing need to develop methods of personalizing visual content, focused on the individual preferences of users. Research methods. Neural collaborative filtering method, generalized matrix factorization method, mood analysis on video. The purpose of the article: Researching the possibilities of improving the personalization of visual content in interactive art by evaluating the emotional reactions of users and their implicit feedback. The results obtained. The application of neural collaborative filtering and generalized matrix factorization to create adapted visual content in interactive art in AR was considered, which will significantly increase the relevance and immersion of users in interactive works. Conclusion. The considered approach can be used to improve immersiveness and personalization during user interaction with interactive art in AR.
基于个人用户偏好的增强现实互动艺术视觉内容的个性化定制
主题性。随着 AR 技术的发展及其在互动艺术中的应用,人们越来越需要开发针对用户个人喜好的个性化视觉内容的方法。研究方法。神经协同过滤法、广义矩阵因式分解法、视频情绪分析法。文章的目的通过评估用户的情绪反应及其隐性反馈,研究改进互动艺术中视觉内容个性化的可能性。研究结果考虑应用神经协同过滤和广义矩阵因式分解在 AR 互动艺术中创建适配的视觉内容,这将显著提高用户在互动作品中的相关性和沉浸感。结论。所考虑的方法可用于在用户与 AR 中的互动艺术进行交互时提高沉浸感和个性化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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