Computer Science ›› 2020, Vol. 47 ›› Issue (7): 47-55.doi: 10.11896/jsjkx.200200114

Special Issue: Big Data & Data Scinece

• Database & Big Data & Data Science • Previous Articles     Next Articles

Techniques for Recommendation System:A Survey

LIU Jun-liang, LI Xiao-guang   

  1. College of information,Liaoning University,Shenyang 110036,China
  • Received:2020-02-25 Online:2020-07-15 Published:2020-07-16
  • About author:LIU Jun-liang,born in 1996,postgra-duate.His main research interests include recommended algorithm and reinforcement learning.
    LI Xiao-guang,born in 1973,Ph.D,professor,is a member of China Computer Federation.His main research interes-tings include machine learning and data mining.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (U1811261) and Liaoning Education Department Service Local Projects(LFW201705)

Abstract: The recommendation system obtains users’ historical behavior data to predict their preferences,such as web browsing data,purchase records,social network information,users’ geographical location and so on.With the development of computer technology,the recommendation technology is mainly based on user-item data matrix decomposition technology in the early stage.Afterwards,it is gradually integrated with data mining,machine learning,artificial intelligence and other technologies,so as to deeply mine the potential preferences of user behavior and build a more accurate user preference model.The recommendation process also moves from static prediction to real-time recommendation,enriching the recommendation results through real-time interaction with users.This paper mainly reviews the key technologies adopted by the recommendation system in different periods,including content-based filtering technology,collaborative filtering technology,recommendation technology based on deep learning,recommendation technology based on reinforcement learning,recommendation technology based on heterogeneous information network.Finally,this paper analyzes the advantages and disadvantages of key technologies,and then looks forward to the future development of the recommendation system.

Key words: Heterogeneous information network, Matrix factorization, Neural network, Recommendation algorithms, Reinforcement learning

CLC Number: 

  • TP311.5
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