摘要
协同过滤算法已广泛应用在推荐系统中,在实现新异性推荐功能中效果显著,但仍存在数据稀疏、扩展性差、冷启动等问题,需要新的设计思路和技术方法进行优化.近几年,深度学习在图像处理、目标识别、自然语言处理等领域均取得突出成果,将深度神经网络模型与推荐算法结合,为构建新型推荐系统带来新的契机.本文提出一种新式混合神经网络模型,该模型由栈式降噪自编码器和深度神经网络构成,学习得到用户和项目的潜在特征向量以及用户-项目之间的交互行为模型,有效解决数据稀疏问题从而提高系统推荐质量.该推荐算法模型通过MovieLens电影评分数据集测试,实验结果与SVD、PMF等传统推荐算法和经典自编码器模型算法作对比,其推荐质量得到显著提升.
The collaborative filtering algorithm has been widely used in the recommendation system. It has significant effects in implementing the new recommendation function, but there are still problems such as sparse data, poor scalability, cold start, etc. New design ideas and technical methods are needed for optimization. In recent years, deep learning has achieved outstanding results in the fields of image processing, target recognition, and natural language processing. Combining the deep neural network model with the recommendation algorithm has brought a new opportunity for the construction of a new recommendation system. In this study, a new hybrid neural network model is proposed, which consists of stack denoising autoencoder and deep neural network. It learns the potential feature vectors of users and projects and the interaction behavior model between users and projects, effectively solves data sparseness, and thus improves the quality of system recommendations. The recommended algorithm model is tested by the MovieLens film scoring data set. The experimental results are compared with traditional recommendation algorithms such as SVD, PMF, and classical autoencoder model algorithms, the recommendation quality is significantly improved.
作者
张杰
付立军
刘俊明
ZHANG Jie;FU Li-Jun;LIU Jun-Ming(University of Chinese Academy of Sciences,Beijing 100049,China;Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China)
出处
《计算机系统应用》
2019年第5期161-166,共6页
Computer Systems & Applications
基金
国土资源部大数据科研专项基金(201511079-3)~~
关键词
降噪自编码器
协同过滤
深度学习
denoising autoencoder
collaborative filtering
deep learning
