一个电影推系统基于用户配置文件和人工蜂群优化
Faezeh Rajabi Kouchi1, Sahar Oftadeh Balani2, Amirhossein Esmaeilpour3
1Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Computational intelligence and neuroscience
|November 3, 2023
概括
这项研究引入了一种使用数据挖掘的新电影推算法. 这种新的方法提高了1.39%的推精度,并使用优化的特征提取和用户相似度指标提高了0.8%的回忆.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 个性化的电影推对于用户参与至关重要.
- 现有的推系统在准确捕捉用户偏好方面面临挑战.
研究的目的:
- 提出一个新的电影推算法.
- 为了提高电影推的精度和回忆力.
主要方法:
- 利用数据挖掘技术进行用户配置文件预处理和特征提取.
- 采用蜂群优化算法进行最佳特征选择.
- 使用提取的特征和欧几里德距离来生成建议计算用户相似性.
主要成果:
- 拟议的算法显示平均精度提高了1.39%.
- 与现有方法相比,该算法实现了回忆的平均增加0.8%.
- 使用MovieLens数据库进行了评估,验证了算法的有效性.
结论:
- 开发的算法为电影推提供了更有效的方法.
- 该方法通过优化特征选择和相似性测量成功提高了推准确性.
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