PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network
PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network
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DOI：
10.1145/3447548.3467234
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Yao Zhou;Jianpeng Xu;Jun Wu;Zeinab Taghavi Nasrabadi;Evren Körpeoglu;Kannan Achan;Jingrui He
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文献类型：
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作者：
Yao Zhou;Jianpeng Xu;Jun Wu;Zeinab Taghavi Nasrabadi;Evren Körpeoglu;Kannan Achan;Jingrui He
随着在线数据量的不断增长，推荐系统是用于信息过滤的强大工具。尽管它在各种网络应用和个性化产品中取得了成功并被广泛采用，但许多现有的推荐系统仍然存在多个缺陷，例如大量未观察到的反馈、模型收敛性差等。现有工作的这些缺陷主要是由于以下两个原因：首先，广泛使用的负采样策略将未标记的条目视为负样本，在现实环境中是无效的；其次，所有训练样本都是从离散观察中获取的，没有学习到用户和项目的潜在真实分布。在本文中，我们通过开发一个名为PURE的新颖框架来解决这些问题，该框架训练一个无偏的正 - 未标记判别器，以区分真正相关的用户 - 项目对和不相关的对，以及一个学习潜在用户 - 项目连续分布的生成器。为了进行全面比较，我们考虑了来自5种不同推荐方法类别的14种流行基准。在两个公开的现实世界数据集上进行的大量实验表明，PURE在8个基于排名的评估指标方面取得了最佳性能。
Recommender systems are powerful tools for information filtering with the ever-growing amount of online data. Despite its success and wide adoption in various web applications and personalized products, many existing recommender systems still suffer from multiple drawbacks such as large amount of unobserved feedback, poor model convergence, etc. These drawbacks of existing work are mainly due to the following two reasons: first, the widely used negative sampling strategy, which treats the unlabeled entries as negative samples, is invalid in real-world settings; second, all training samples are retrieved from the discrete observations, and the underlying true distribution of the users and items is not learned. In this paper, we address these issues by developing a novel framework named PURE, which trains an unbiased positive-unlabeled discriminator to distinguish the true relevant user-item pairs against the ones that are non-relevant, and a generator that learns the underlying user-item continuous distribution. For a comprehensive comparison, we considered 14 popular baselines from 5 different categories of recommendation approaches. Extensive experiments on two public real-world data sets demonstrate that PURE achieves the best performance in terms of 8 ranking based evaluation metrics.