基于深度学习的电影推用户偏好建模
Yang Gao1,2, Hong Zheng3, Haonan Cui4
1Department of Performing Arts and Culture, Catholic University, 43 Jifeng Road, Yuanmei District (Sacred Heart Campus), Bucheon, Kyonggi, 070-765, Republic of Korea.
Scientific reports
|May 13, 2025
概括
这项研究引入了一种新的电影推深度学习方法,通过分析用户行为和电影内容来改进个性化. 基于人工智能的方法提高了推准确性和回忆力,以获得更好的用户体验.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 信息检索 信息检索
背景情况:
- 传统的电影推系统与复杂的用户偏好作斗争.
- 现有的方法,如基于内容和协作过的方法有局限性.
- 个性化的电影推需要先进的技术来捕捉用户动态.
研究的目的:
- 引入一种基于深度学习的新方法,用于增强电影推模型.
- 解决当前推系统在捕捉用户偏好方面的局限性.
- 为了在电影推中实现更高的定制度.
主要方法:
- 利用人工智能 (AI),基于图形的技术和文本挖掘.
- 使用PageRank来根据用户浏览历史记录对电影进行排名的重要性.
- 使用卷积神经网络 (CNN) 来预测用户对电影的接受.
主要成果:
- 这种新方法在建议准确性和回忆方面显著提高.
- 实现了7.15%的精度增加和5.19%的回忆增加.
- 在215名用户和508个电影页面的数据集上进行的实验评估.
结论:
- 拟议的人工智能驱动的方法比现有的电影推系统提供了实质性的改进.
- 该技术有效地分析用户行为和电影内容,以提供高度个性化的建议.
- 这些发现表明,在现实世界个性化电影推平台中实施这种方法具有很大的潜力.
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