一个基于深度学习的混合推模型,用于互联网用户
Amany Sami1, Waleed El Adrousy2, Shahenda Sarhan2
1Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt. engamanysami@gmail.com.
Scientific reports
|November 26, 2024
概括
本研究介绍了HRS-IU-DL模型,这是一个混合推系统,通过结合多种技术来提高准确性和相关性. 它有效地解决了数据稀疏性和冷启动问题等挑战,以获得更好的个性化建议.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 传统的推系统 (RS) 在准确性,可扩展性,效率和处理冷启动问题方面存在局限性.
- 个性化的项目建议至关重要,但现有的方法往往不足.
研究的目的:
- 介绍HRS-IU-DL模型,这是一个新的混合推系统,旨在提高准确性和相关性.
- 解决推系统的关键挑战,包括数据稀疏性和冷启动问题.
主要方法:
- 该HRS-IU-DL模型集成了基于用户和基于项目的协作过 (CF),神经协作过 (NCF) 和循环神经网络 (RNN).
- 用术语频率-反向文档频率 (TF-IDF) 的基于内容的过 (CBF) 用于项目属性分析.
- 使用N-Sample技术,Cosine相似性,Singular Value Decomposition (SVD) 和TF-IDF来根据用户指定的类型推类似的项目.
主要成果:
- 在Movielens 100k数据集上,HRS-IU-DL模型与最先进的方法相比显示出更高的性能.
- 在关键评估指标中观察到显著的改善,包括根平均平方误差 (RMSE),平均绝对误差 (MAE),精度和回忆.
结论:
- 拟议的HRS-IU-DL模型有效地克服了传统推系统的局限性.
- 这种混合方法在个性化推技术方面取得了实质性的进步,有效地解决了稀缺性和冷启动问题.
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