基于数学矩阵分解的人工智能推系统中的跨领域信息融合和个性化推
Xiaoyan Meng1,2,3
1Inner Mongolia Youth College of Political Science of Inner Mongolia Normal University, Hohhot, 010051, China. 20178012@imnu.edu.cn.
Scientific reports
|April 3, 2024
概括
本研究介绍了一个跨领域的信息融合矩阵分解算法,通过解决人工智能系统中的数据稀疏性来改进个性化的建议. 与传统模型相比,这种新的方法显著提高了推的准确性和多样性.
科学领域:
- 人工智能的人工智能
- 信息检索 信息检索
- 数据科学数据科学数据科学
背景情况:
- 协作过算法面临的挑战是域间信息融合和数据稀疏性.
- 提高个性化的推准确性对于人工智能 (AI) 推系统至关重要.
研究的目的:
- 提出一个跨领域的信息融合矩阵分解算法,以改进个性化的建议.
- 解决数据稀疏性和提高AI推系统的准确性.
主要方法:
- 收集了杜班电影评分和社交网络数据.
- 利用Levenstein距离来删除重复的分数和自然语言处理来提取文本特征.
- 采用图形卷积网络用于用户关系特征向量,离散数据的热编码和Ridge规范化以防止过拟合.
- 综合功能使用加权平均和功能连接,结合基于项目的协作过和合并的用户特征.
主要成果:
- 跨领域信息融合优化显著提高了四种矩阵分解算法 (ALS,NMF,SVD,LFM) 的得分准确度.
- 平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 分别减少了12.8%和13.2%.
- 平均F1得分达到0.97 (k=10),而LFM的排名准确度覆盖率增加了54.2%.
结论:
- 拟议的矩阵分解算法与跨域信息融合提供了卓越的准确性,预测性能,推多样性和排名质量.
- 这种方法有效地克服了协作过的局限性,在建议准确性和多样性方面表现优于传统模型.
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