Recently recommender systems become more and more significant in the daily life such as event recommendation, content recommendation and commodity recommendation, and so forth. Although the recommender systems based on the generative adversarial network (GAN) are competent, the user trust information is seldom taken into consideration to improve the recommendation accuracy. In this paper, we propose a Trust‐Aware GAN with recurrent neural network (RNN) for RECommender systems named TagRec, which makes use of the user trust information for top‐N recommendation. In the framework, the discriminative model is a multilayer perceptron to distinguish whether a sample is from the real data or fake data generated by the generative model. The discriminator helps to guide the training of the generative model to make it fit the data distribution of the user trust information. The generative model is a RNN with long short‐term memory cells, aiming to confuse the discriminative model by generating samples as similar as possible to the real data. Through the adversarial training between the discriminative and generative models, the user trust information can be fully used to improve the recommendation performance. We conduct extensive experiments on real‐word data sets to validate the effectiveness of the TagRec by comparing it with the benchmarks.

中文翻译：

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用于推荐系统的具有循环神经网络的信任感知生成对抗网络

最近推荐系统在日常生活中变得越来越重要，例如事件推荐、内容推荐和商品推荐等等。尽管基于生成对抗网络（GAN）的推荐系统是有能力的，但很少考虑用户信任信息来提高推荐精度。在本文中，我们为名为 TagRec 的 RECommender 系统提出了一个带有循环神经网络 (RNN) 的 Trust-Aware GAN，它利用用户信任信息进行 top-N 推荐。在该框架中，判别模型是一个多层感知器，用于区分样本是来自真实数据还是生成模型生成的假数据。鉴别器有助于指导生成模型的训练，使其适合用户信任信息的数据分布。生成模型是具有长短期记忆单元的 RNN，旨在通过生成与真实数据尽可能相似的样本来混淆判别模型。通过判别模型和生成模型之间的对抗训练，可以充分利用用户信任信息来提高推荐性能。我们对真实词数据集进行了大量实验，通过将其与基准进行比较来验证 TagRec 的有效性。通过判别模型和生成模型之间的对抗训练，可以充分利用用户信任信息来提高推荐性能。我们对真实词数据集进行了大量实验，通过将其与基准进行比较来验证 TagRec 的有效性。通过判别模型和生成模型之间的对抗训练，可以充分利用用户信任信息来提高推荐性能。我们对真实词数据集进行了大量实验，通过将其与基准进行比较来验证 TagRec 的有效性。