摘要
随着互联网技术的快速发展,如何对海量网络信息进行挖掘分析,已成为热点和难点问题。推荐系统能够帮助用户在没有明确需求或者信息量巨大时解决信息过载的问题,为用户提供精准、快速的业务(如商品、项目、服务等)信息,成为近年来产业界和学术界共同的兴趣点和研究热点,但是,目前数据的种类多种多样并且应用场景广泛,在面对这种情况时,推荐系统也会遇到冷启动、稀疏矩阵等挑战。深度学习是机器学习的一个重要研究领域和分支,近年来发展迅猛。研究人员使用深度学习方法,在语音识别、图像处理、自然语言处理等领域都取得了很大的突破与成就。目前,深度学习在推荐领域也得到了许多研究人员的青睐,成为推荐领域的一个新方向。推荐方法中融合深度学习技术,可以有效解决传统推荐系统中冷启动、稀疏矩阵等问题,提高推荐系统的性能和推荐精度。文中主要对传统的推荐方法和当前深度学习技术中神经网络在推荐方法上的应用进行了归纳,其中传统推荐方法主要分为以下3类:1)基于内容推荐方法主要依据用户与项目之间的特征信息,用户之间的联系不会影响推荐结果,所以不存在冷启动和稀疏矩阵的问题,但是基于内容推荐的结果新颖程度低并且面临特征提取的问题。2)协同过滤推荐方法是目前应用最为广泛的一种方法,不需要有关用户或项目的信息,只基于用户和诸如点击、浏览和评级等项目的交互信息做出准确的推荐。虽然该方法简单有效但是会出现稀疏矩阵和冷启动的问题。3)混合推荐方法融合了前2种传统推荐方法的特点,能取得很好的推荐效果,但在处理文本、图像等多源异构辅助信息时仍面临一些挑战与困难。依据神经网络基于深度学习的推荐方法主要分为4类:基于深度神经网络(DNN)的推荐方法、基于卷积神经网络(CNN)�
With the rapid development of the internet,how to mine and analyze massive network information has become a recognized hot and difficult problem.Among them,the recommendation system can provide users with accurate and fast business(commodities,projects,services,etc.)information,which is the common interest and research hotspot of industry and academia in recent years.A recommendation system can help users to solve the problem of information overload when there is no clear demand or a large amount of information.However,at present,the types of data are diverse and the application scenarios are extensive.When faced with this situation,the recommendation system also encounters challenges such as cold start and sparse matrix.Deep learning is an important research field and the most important branch of machine learning.In recent years,deep learning has developed rapidly.Researchers have made great breakthroughs and achievements in speech recognition,image processing,natural language processing and other fields by using deep learning.At present,deep learning has also been favored by a large number of researchers in the field of recommendation and has become a new direction.Incorporating deep learning technology into the recommendation method can effectively solve the problems of cold start and sparse matrix in traditional recommendation systems,and improve the performance and recommendation accuracy of the recommendation system.This paper mainly summarizes the application of traditional recommendation methods and the application of neural network in current deep learning technology in recommendation methods,among which the traditional recommendation methods can be divided into the following three categories:1)Content-based recommendation methods is mainly based on the feature information between the user and the project.The connection between users will not affect the recommendation result,so there is no problem of cold start and sparse matrix,but the content-based recommendation results are low in novelty and face the
作者
周万珍
曹迪
许云峰
刘滨
ZHOU Wanzhen;CAO Di;XU Yunfeng;LIU Bin(School of Information Science and Engineering,Hebei University of Science and Technology,Shijiazhuang,Hebei 050018,China;School of Economics and Management,Hebei University of Science and Technology,Shijiazhuang,Hebei 050018,China;Big Data and Social Computing Research Center,Hebei University of Science and Technology,Shijiazhuang,Hebei 050018,China)
出处
《河北科技大学学报》
CAS
2020年第1期76-87,共12页
Journal of Hebei University of Science and Technology
基金
河北省科技支撑计划项目(17210104D,18210109D)
河北省高等学校科学技术研究项目(ZD2015099)
河北省高层次人才资助项目(A2016002015)
关键词
计算机神经网络
推荐系统
数据挖掘
深度学习
信息过载
computer neural network
recommendation system
data mining
deep learning
information overload
