@inproceedings{b8110cd90f3b41e8b4a8b8fd817d195c,
title = "D2D-TM: A Cycle VAE-GAN for Multi-Domain Collaborative Filtering",
abstract = "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.",
keywords = "Collaborative Filtering, Cycle consistency, Deep Learning, General Adversarial Network, Recommender System, Variational Autoencoder",
author = "Linh Nguyen and Tsukasa Ishigaki",
note = "Funding Information: ACKNOWLEDGEMENT This work was supported by JSPS KAKENHI Grant Numbers 17K03988 Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",
year = "2019",
month = dec,
doi = "10.1109/BigData47090.2019.9006461",
language = "English",
series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1175--1180",
editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",
booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
address = "United States",
}