Domain-to-Domain Translation Model for Recommender System
Domain-to-Domain Translation Model for Recommender System
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发表时间：
2018-12
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通讯作者：
Linh Nguyen;Tsukasa Ishigaki
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作者：
Linh Nguyen;Tsukasa Ishigaki
近年来，多领域推荐系统由于能够解决冷启动问题和支持交叉销售而受到研究者的广泛关注。然而，当应用于多领域项目时，虽然专门针对单个领域的算法在捕获每个领域的特定特征方面有很多困难，但多领域算法很少有机会获得领域之间的相似特征。由于域之间存在相似性和差异性，因此多域模型必须同时捕获这两种相似性和差异性，才能获得良好的性能。其他多领域系统的研究仅仅是将知识从源领域转移到目标领域，因此源领域通常来自外部因素，如搜索查询或社交网络，有时无法获得。为了解决这两个问题，我们提出了一个模型，可以提取域之间的同质和分歧的功能，并提取数据在一个领域可以支持其他领域同样的：一个所谓的域到域翻译模型（D2 D-TM）。它基于生成对抗网络（GAN），变分自编码器（VAE）和用于权重共享的循环一致性（CC）。我们使用每个域的用户交互历史作为输入，并通过VAE-GAN-CC网络提取潜在特征。实验强调了所提出的系统的有效性在国家的最先进的方法由一个大的利润。
Recently multi-domain recommender systems have received much attention from researchers because they can solve cold-start problem as well as support for cross-selling. However, when applying into multi-domain items, although algorithms specifically addressing a single domain have many difficulties in capturing the specific characteristics of each domain, multi-domain algorithms have less opportunity to obtain similar features among domains. Because both similarities and differences exist among domains, multi-domain models must capture both to achieve good performance. Other studies of multi-domain systems merely transfer knowledge from the source domain to the target domain, so the source domain usually comes from external factors such as the search query or social network, which is sometimes impossible to obtain. To handle the two problems, we propose a model that can extract both homogeneous and divergent features among domains and extract data in a domain can support for other domain equally: a so-called Domain-to-Domain Translation Model (D2D-TM). It is based on generative adversarial networks (GANs), Variational Autoencoders (VAEs), and Cycle-Consistency (CC) for weight-sharing. We use the user interaction history of each domain as input and extract latent features through a VAE-GAN-CC network. Experiments underscore the effectiveness of the proposed system over state-of-the-art methods by a large margin.