计算机科学
推荐系统
修补
图像翻译
图像(数学)
人气
协同过滤
测距
翻译(生物学)
人工智能
理论计算机科学
机器学习
信使核糖核酸
化学
基因
社会心理学
电信
生物化学
心理学
作者
Ervin Dervishaj,Paolo Cremonesi
标识
DOI:10.1145/3477314.3507099
摘要
Proposed in 2014, Generative Adversarial Networks (GAN) initiated a fresh interest in generative modelling. They immediately achieved state-of-the-art in image synthesis, image-to-image translation, text-to-image generation, image inpainting and have been used in sciences ranging from medicine to high-energy particle physics. Despite their popularity and ability to learn arbitrary distributions, GAN have not been widely applied in recommender systems (RS). Moreover, only few of the techniques that have introduced GAN in RS have employed them directly as a collaborative filtering (CF) model.
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