D2D-TM: A Cycle VAE-GAN for Multi-Domain Collaborative Filtering

Linh Nguyen, Tsukasa Ishigaki

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Multi-domain recommender systems can solve cold-start problems and can support cross-selling of products and services. We propose a model to address these difficulties by extracting homogeneous and divergent features from domains. Our Domain-to-Domain Translation Model (D2D-TM), which is based on generative adversarial networks (GANs) and variational autoencoders (VAEs), uses the user interaction history. Domain cycle consistency (CC) constrains the inter-domain relations. Results obtained from experimentation demonstrate the great effectiveness of the proposed system when compared to several state-of-the-art systems.

本文言語English
ホスト出版物のタイトルProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
編集者Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1175-1180
ページ数6
ISBN(電子版)9781728108582
DOI
出版ステータスPublished - 2019 12月
イベント2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
継続期間: 2019 12月 92019 12月 12

出版物シリーズ

名前Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference2019 IEEE International Conference on Big Data, Big Data 2019
国/地域United States
CityLos Angeles
Period19/12/919/12/12

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • 情報システム
  • 情報システムおよび情報管理

フィンガープリント

「D2D-TM: A Cycle VAE-GAN for Multi-Domain Collaborative Filtering」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル