• Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
搜索

Author:

He, Siyuan (He, Siyuan.) | Li, Tao (Li, Tao.) | Duan, Yuxin (Duan, Yuxin.) | Yang, Zhenning (Yang, Zhenning.) | Li, Feixiang (Li, Feixiang.)

Indexed by:

EI Scopus

Abstract:

the recommendation system is one of the core tasks of data mining. It divided into the recommendation system for explicit feedback and the recommendation system for implicit feedback. In recent years, many researchers have combined deep learning with recommendation system of explicit feedback and achieved excellent results. But it is very rare for implicit feedback. In this paper, we apply deep learning to the recommendation system for implicit feedback, and propose a new model that is combined with Neural Collaborative Filtering and Variable automatic-encoder. We use the MovieLens dataset to evaluate our proposed model. Experimental results show that the proposed model effectively improves the accuracy and quality of the recommended results, the Precision is 0.715 and the NDCG is 0.436 without manual parameters. © 2019 IEEE.

Keyword:

Recommender systems Collaborative filtering Data mining Signal encoding Deep learning Learning systems

Author Community:

  • [ 1 ] [He, Siyuan]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, China
  • [ 2 ] [Li, Tao]School of Software Engineering, University of Science and Technology of China, China
  • [ 3 ] [Duan, Yuxin]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, China
  • [ 4 ] [Yang, Zhenning]School of Software Engineering, Beijing University of Technology, China
  • [ 5 ] [Li, Feixiang]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, China

Reprint Author's Address:

Email:

Show more details

Related Keywords:

Related Article:

Source :

Year: 2019

Page: 512-516

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 3

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

Online/Total:499/10555395
Address:BJUT Library(100 Pingleyuan,Chaoyang District,Beijing 100124, China Post Code:100124) Contact Us:010-67392185
Copyright:BJUT Library Technical Support:Beijing Aegean Software Co., Ltd.