基于TransD模型和AIGC授权的电影推算法及其应用有效性分析分析
1Department of Cinematography, Beijing Film Academy, Beijing, China.
PloS one
|November 11, 2025
概括
本研究介绍了一种使用知识图嵌入 (TransD) 和人工智能生成内容 (AIGC) 的新电影推框架,以改善语义理解和用户兴趣建模,显著优于传统方法,特别是对于新用户.
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:
- 传统的推系统在冷启动,有限的语义理解和用户利益表示方面扎.
- 现有的模型通常依赖于手动标签,无法充分利用结构化信息或不同的用户兴趣.
研究的目的:
- 通过解决冷启动问题,改善语义理解和建模用户兴趣多样性来增强推系统.
- 为电影推提出一个新的框架,将知识图嵌入 (TransD) 和人工智能生成内容 (AIGC) 整合在一起.
主要方法:
- 利用TransD用于知识图中异质实体和关系的动态语义建模.
- 使用AIGC从用户评论中提取隐藏的兴趣维度,情感特征和语义标签,以构建个人资料.
- 构建了一个内容标签完成系统和一个高维的用户兴趣配置文件.
主要成果:
- 拟议的模型在MovieLens数据集中实现了卓越的性能,成功率高达0.878%,平均精度 (MAP) 高达0.637.
- 展示了高的用户满意度得分 (高达0.89) 和点击率 (CTR) (高达0.35),明显优于传统模型.
- 展示了卓越的稳定性和语义适应性,特别是对于冷启动用户和兴趣转换.
结论:
- 综合方法有效地将结构化和非结构化信息结合在一起,以提供先进的电影推.
- 该研究为智能推系统,知识图嵌入和基于AIGC的混合建模提供了重要的理论和实践贡献.
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