ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2019, Vol. 56 ›› Issue (8): 1661-1669.doi: 10.7544/issn1000-1239.2019.20190178

所属专题: 2019人工智能前沿进展专题

• 人工智能 • 上一篇    下一篇

一种基于两阶段深度学习的集成推荐模型

王瑞琴1,吴宗大2,蒋云良1,楼俊钢1   

  1. 1(湖州师范学院信息工程学院 浙江湖州 313000);2(温州大学瓯江学院 浙江温州 325035) (zongda1983@163.com)
  • 出版日期: 2019-08-01
  • 基金资助: 
    教育部人文社科规划基金项目(19YJA870013);国家自然科学基金项目(61403338);浙江省科技计划重点研发项目(2017C03047)

An Integrated Recommendation Model Based on Two-stage Deep Learning

Wang Ruiqin1, Wu Zongda2, Jiang Yunliang1, Lou Jungang1   

  1. 1(School of Information Engineering, Huzhou University, Huzhou, Zhejiang 313000);2(School of Oujiang, Wenzhou University, Wenzhou, Zhejiang 325035)
  • Online: 2019-08-01

摘要: 近年来,深度学习技术被广泛应用于推荐系统领域并获得了很大的成功,然而深度学习模型的输入质量对学习结果具有很大影响,稀疏的输入特征向量不仅会增加后续模型训练的难度,而且容易导致学习结果落入局部最优.提出一个基于两阶段深度学习的集成推荐模型:首先,利用具有封闭式参数计算能力的边缘化堆叠去噪自动编码机进行用户和项目高层抽象特征的提取;然后,将得到的用户抽象特征和项目抽象特征进行连接并作为深度神经网络模型的输入向量,通过联合训练的方式进行参数学习和模型优化.此外,为了对低阶特征交互进行建模,推荐模型中还集成了基于原始特征向量的逻辑回归模型.在通用数据集上的大量对比实验研究表明:与当前流行的深度学习推荐方法相比,该方法在推荐精度和召回率方面都有所改善,甚至是在数据稀疏和冷启动的环境下.

关键词: 深度学习, 边缘化堆叠去噪自动编码机, 深度神经网络, 特征提取

Abstract: In recent years, deep learning technology has been widely used in the field of recommendation systems and has achieved great success. However, the input quality of the deep learning models has a great influence on the learning results. A sparse input feature vector will not only increase the difficulty of subsequent model training, but also will lead to the learning results falling into local optimum. In this article, an integrated recommendation model based on two-stage deep learning is proposed. Firstly, two individual marginal stacked denoising auto-encoders (mSDA) models with closed-form parameter calculation are used to extract the high-level abstract features of the users and the items. Then the resulted user abstract feature and the item abstract feature are connected as the input vector of the deep neural network (DNN) model, and the parameter learning and model optimization are performed through joint training. In addition, in order to model low-order feature interactions, a logistic regression model based on original feature vector is also integrated into the recommendation model. Extensive experiments with two real-world datasets indicate that the proposed recommendation model shows excellent recommendation performance compared with the state-of-the-art methods, especially in the data sparse and the cold start environments.

Key words: deep learning, marginalized stacked denoising auto-encoder, deep neural network (DNN), feature extraction

中图分类号: