D2D-TM: A Cycle VAE-GAN for Multi-Domain Collaborative Filtering
D2D-TM: A Cycle VAE-GAN for Multi-Domain Collaborative Filtering
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DOI：
10.1109/bigdata47090.2019.9006461
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Linh Nguyen;Tsukasa Ishigaki
中科院分区：
文献类型：
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作者：
Linh Nguyen;Tsukasa Ishigaki
多领域推荐系统可以解决冷启动问题，并支持产品和服务的交叉销售。我们提出了一个模型，以解决这些困难，从域中提取同质和不同的功能。我们的域到域翻译模型（D2 D-TM）基于生成对抗网络（GAN）和变分自编码器（VAE），使用用户交互历史。域循环一致性（CC）约束域间关系。从实验中得到的结果表明，所提出的系统相比，几个国家的最先进的系统具有很大的有效性。
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.