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Author:

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

Indexed by:

CPCI-S

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.

Keyword:

Variable automatic-encoder Implicit Feedback Deep learning Recommended System Neural Collaborative Filtering

Author Community:

  • [ 1 ] [He, Siyuan]Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China
  • [ 2 ] [Duan, Yuxin]Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China
  • [ 3 ] [Li, Feixiang]Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China
  • [ 4 ] [Li, Tao]Univ Sci & Technol China, Sch Software Engn, Hefei, Anhui, Peoples R China
  • [ 5 ] [Yang, Zhenning]Beijing Univ Technol, Sch Software Engn, Beijing, Peoples R China

Reprint Author's Address:

  • [He, Siyuan]Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China

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Source :

PROCEEDINGS OF 2019 IEEE 8TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC 2019)

Year: 2019

Page: 512-516

Language: English

Cited Count:

WoS CC Cited Count: 2

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 8

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