Collaborative Multi-key Learning with an Anonymization Dataset for a Recommender System
Collaborative Multi-key Learning with an Anonymization Dataset for a Recommender System
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DOI：
10.1109/ijcnn.2019.8852157
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发表时间：
2019-07
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
在推荐系统中，准确性和隐私性的平衡是一个重要的权衡问题。为了达到高性能，现代推荐系统倾向于使用尽可能多的信息。越来越多的研究证明了这一趋势的混合方法，结合联合收割机评级和辅助信息。然而，由于隐私问题，在许多情况下，服务提供商不能要求用户提供他们的个人信息。因此，许多早期报道的方法只使用项目属性作为辅助信息。为了解决这些缺点，我们的手稿提供了一种方法来提取用户配置文件，而不使用人口统计数据。我们的模型通过两个独立的深度神经网络来学习用户和项目的潜在变量，并使用信息及其评级来学习用户和项目之间的隐式关系。实验证明，我们的模型是一个更有效的推荐系统比国家的最先进的基线。
Balancing accuracy and privacy is an important tradeoff problem for information systems, including recommender systems. To achieve high performance, modern recommender systems tend to use as much information as possible. This trend is borne out by the increasing number of studies of hybrid methods that combine rating and auxiliary information. However, because of privacy concerns, in many cases, service providers can not require users to give their personal information. Therefore, numerous earlier reported methods only use item attributes for auxiliary information. To address these shortcomings, our manuscript provides a method to extract user profiles without using demographic data. Our model learns user and item latent variables through two separate deep neural networks and also learns implicit relations between users and items using the information and their ratings. Experiments verified that our model is a more effective recommender system than state-of- the-art baselines.