Predicting new adopters via socially-aware neural graph collaborative filtering

Yu Che Tsai, Muzhi Guan, Cheng Te Li, Meeyoung Cha, Yong Li, Yue Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

We predict new adopters of specific items by proposing S-NGCF, a socially-aware neural graph collaborative filtering model. This model uses information about social influence and item adoptions; then it learns the representation of user-item relationships via a graph convolutional network. Experiments show that social influence is essential for adopter prediction. S-NGCF outperforms the prediction of new adopters compared to state-of-the-art methods by 18%.

Original languageEnglish
Title of host publicationComputational Data and Social Networks - 8th International Conference, CSoNet 2019, Proceedings
EditorsAndrea Tagarelli, Hanghang Tong
PublisherSpringer
Pages155-162
Number of pages8
ISBN (Print)9783030349790
DOIs
Publication statusPublished - 2019
Event8th International Conference on Computational Data and Social Networks, CSoNet 2019 - Ho Chi Minh City, Viet Nam
Duration: 2019 Nov 182019 Nov 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11917 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Computational Data and Social Networks, CSoNet 2019
Country/TerritoryViet Nam
CityHo Chi Minh City
Period19-11-1819-11-20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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