Recently introduced EASE algorithm presents a simple and elegant way, how to solve the top-N recommendation task. In this paper, we introduce Neural EASE to further improve the performance of this algorithm by incorporating techniques for training modern neural networks. Also, there is a growing interest in the recsys community to utilize variational autoencoders (VAE) for this task. We introduce deep autoencoder FLVAE benefiting from multiple non-linear layers without an information bottleneck while not overfitting towards the identity. We show how to learn FLVAE in parallel with Neural EASE and achieve the state of the art performance on the MovieLens 20M dataset and competitive results on the Netflix Prize dataset.
翻译:最近引入的 EASE 算法是一个简单和优雅的方法, 如何解决最高级建议任务。 在本文中, 我们引入神经 EASE, 以便通过引入现代神经网络培训技术来进一步提高这一算法的绩效。 另外, 康复者社区越来越有兴趣利用变异自动计算器( VAE ) 来完成这项任务。 我们引入了从多个非线性层中受益的深自动编码器 FLVAE, 没有信息瓶颈, 同时又不过度适应身份。 我们展示了如何在与 NEURA EASE 同步学习 FLVAE, 并在 Netflix 奖数据集上实现电影20M数据集的艺术表现和竞争结果 。