论文标题
与隐式反馈的推荐联合变异自动编码器
Joint Variational Autoencoders for Recommendation with Implicit Feedback
论文作者
论文摘要
变异自动编码器（VAE）最近在与隐式反馈的协作过滤中表现出了有希望的性能。这些现有建议模型学习用户表示以重建或预测用户偏好。我们介绍了联合变量自动编码器（JOVA），这是两个VAE的合奏，其中VAE共同学习用户和项目表示，并共同重建并预测用户偏好。这种设计使Jova可以同时捕获用户使用者和项目项目相关性。通过通过基于铰链的成对损耗函数（JOVA-HINGE）扩展JOVA的目标函数，我们将其进一步专门为TOP-K推荐，并具有隐式反馈。我们在几个现实世界数据集上进行的广泛实验表明，在各种常用的指标下，Jova-Hinge的表现优于一系列最先进的协作过滤方法。我们的经验结果还证实了JOVA-HINGE在现有方法中，对于训练数据数量有限的寒冷启动用户的表现。
Variational Autoencoders (VAEs) have recently shown promising performance in collaborative filtering with implicit feedback. These existing recommendation models learn user representations to reconstruct or predict user preferences. We introduce joint variational autoencoders (JoVA), an ensemble of two VAEs, in which VAEs jointly learn both user and item representations and collectively reconstruct and predict user preferences. This design allows JoVA to capture user-user and item-item correlations simultaneously. By extending the objective function of JoVA with a hinge-based pairwise loss function (JoVA-Hinge), we further specialize it for top-k recommendation with implicit feedback. Our extensive experiments on several real-world datasets show that JoVA-Hinge outperforms a broad set of state-of-the-art collaborative filtering methods, under a variety of commonly-used metrics. Our empirical results also confirm the outperformance of JoVA-Hinge over existing methods for cold-start users with a limited number of training data.
