论文标题
适用于顶级项目的条件变异自动编码器
Conditioned Variational Autoencoder for top-N item recommendation
论文作者
论文摘要
在本文中，我们提出了一个条件变分的自动编码器（C-VAE），以限制上n个项目建议，其中推荐的项目必须满足给定的条件。所提出的模型架构类似于条件向量被馈入编码器的标准VAE。由于新的重建损失将输入条件考虑在培训期间，在培训期间学习了受限的排名。我们表明，我们的模型概括了最先进的多VAE协作过滤模型。此外，我们提供了有关C-Vae在潜在空间中学到的知识的见解，提供了一种对人类友好的解释。实验结果强调了C-VAE在约束下提供准确建议的潜力。最后，进行的分析表明，C-VAE可以在其他建议方案中使用，例如上下文感知的建议。
In this paper, we propose a Conditioned Variational Autoencoder (C-VAE) for constrained top-N item recommendation where the recommended items must satisfy a given condition. The proposed model architecture is similar to a standard VAE in which the condition vector is fed into the encoder. The constrained ranking is learned during training thanks to a new reconstruction loss that takes the input condition into account. We show that our model generalizes the state-of-the-art Mult-VAE collaborative filtering model. Moreover, we provide insights on what C-VAE learns in the latent space, providing a human-friendly interpretation. Experimental results underline the potential of C-VAE in providing accurate recommendations under constraints. Finally, the performed analyses suggest that C-VAE can be used in other recommendation scenarios, such as context-aware recommendation.
