Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (6): 1898-1913.DOI: 10.11772/j.issn.1001-9081.2021040607

• Data science and technology • Previous Articles    

Review of recommendation system

Meng YU, Wentao HE, Xuchuan ZHOU(), Mengtian CUI, Keqi WU, Wenjie ZHOU   

  1. The Key Laboratory for Computer Systems of State Ethnic Affairs Commission (Southwest Minzu University),Chengdu Sichuan 610041,China
  • Received:2021-04-19 Revised:2021-07-14 Accepted:2021-07-20 Online:2022-06-22 Published:2022-06-10
  • Contact: Xuchuan ZHOU
  • About author:YU Meng, born in 1995, M. S. candidate. Her research interests include recommendation system, information filtering, data mining.
    HE Wentao, born in 1996, M. S. candidate. His research interests include deep learning, data mining.
    CUI Mengtian, born in 1972, Ph. D., professor. Her research interests include intelligent information processing.
    WU Keqi, born in 1997, M. S. candidate. His research interests include recommendation system.
    ZHOU Wenjie, born in 1997, M. S. candidate. His research interests include data mining.
  • Supported by:
    National Natural Science Foundation of China(12050410248);Science and Technology Program of Sichuan Province(2021YFH0120);Southwest Minzu University Graduate Innovative Research Project(CX2020SZ04)

推荐系统综述

于蒙, 何文涛, 周绪川(), 崔梦天, 吴克奇, 周文杰   

  1. 计算机系统国家民委重点实验室(西南民族大学),成都 610041
  • 通讯作者: 周绪川
  • 作者简介:于蒙(1995—),女,宁夏固原人,硕士研究生,CCF会员,主要研究方向:推荐系统、信息过滤、数据挖掘
    何文涛(1996—),男,湖南永州人,硕士研究生,主要研究方向:深度学习、数据挖掘
    崔梦天(1972—),女,内蒙古乌兰浩特人,教授,博士,主要研究方向:智能信息处理
    吴克奇(1997—),男,湖北孝感人,硕士研究生,主要研究方向:推荐系统
    周文杰(1997—),男,四川广安人,硕士研究生,主要研究方向:数据挖掘。
  • 基金资助:
    国家自然科学基金资助项目(12050410248);四川省科技计划项目(2021YFH0120);西南民族大学研究生创新型科研项目(CX2020SZ04)

Abstract:

With the continuous development of network applications, network resources are growing exponentially and information overload is becoming increasingly serious, so how to efficiently obtain the resources that meet the user needs has become one of the problems that bothering people. Recommendation system can effectively filter mass information and recommend the resources that meet the users needs. The research status of the recommendation system was introduced in detail, including three traditional recommendation methods of content-based recommendation, collaborative filtering recommendation and hybrid recommendation, and the research progress of four common deep learning recommendation models based on Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN) and Graph Neural Network (GNN) were analyzed in focus. The commonly used datasets in recommendation field were summarized, and the differences between the traditional recommendation algorithms and the deep learning-based recommendation algorithms were analyzed and compared. Finally, the representative recommendation models in practical applications were summarized, and the challenges and the future research directions of recommendation system were discussed.

Key words: recommendation algorithm, collaborative filtering, deep learning, Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Graph Neural Network (GNN)

摘要:

随着网络应用的不断发展,网络资源呈指数型增长,信息过载现象日益严重,如何高效获取符合需求的资源成为困扰人们的问题之一。推荐系统能对海量信息进行有效过滤,为用户推荐符合其需求的资源。对推荐系统的研究现状进行详细介绍,包括基于内容的推荐、协同过滤推荐和混合推荐这三种传统推荐方式,并重点分析了基于卷积神经网络(CNN)、深度神经网络(DNN)、循环神经网络(RNN)和图神经网络(GNN)这四种常见的深度学习推荐模型的研究进展;归纳整理了推荐领域常用的数据集,同时分析对比了传统推荐算法和基于深度学习的推荐算法的差异。最后,总结了实际应用中具有代表性的推荐模型,讨论了推荐系统面临的挑战和未来的研究方向。

关键词: 推荐算法, 协同过滤, 深度学习, 卷积神经网络, 深度神经网络, 循环神经网络, 图神经网络

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