Open AccessAnalysis of Recommendation Systems Based on Neural Networks
Author(s) -
Ningxin Song
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1634/1/012051
Subject(s) - recommender system , computer science , information overload , focus (optics) , artificial neural network , service (business) , deep neural networks , information system , data science , world wide web , artificial intelligence , marketing , business , engineering , physics , electrical engineering , optics
As the amount of online information explodes, the recommendation system has evolved into an effective strategy to overcome the problem of information overload, which has become the focus of academic and industrial circles, and has led to other related research results. Through the recommendation methods, the author mines the items including information, service, and goods that the user is interested in, and recommends the results to the user in the form of a personalized list. In this paper, the author mainly examines and summarizes the progress of research on neural network based recommendation systems in recent years, as well as giving conclusions about the differences and benefits between that and traditional algorithms. In the end, the future trend of the recommendation systems based on neural networks is prospected.