  Sequential recommendation as an emerging topic has attracted increasing
attention due to its important practical significance. Models based on deep
learning and attention mechanism have achieved good performance in sequential
recommendation. Recently, the generative models based on Variational
Autoencoder (VAE) have shown the unique advantage in collaborative filtering.
In particular, the sequential VAE model as a recurrent version of VAE can
effectively capture temporal dependencies among items in user sequence and
perform sequential recommendation. However, VAE-based models suffer from a
common limitation that the representational ability of the obtained approximate
posterior distribution is limited, resulting in lower quality of generated
samples. This is especially true for generating sequences. To solve the above
problem, in this work, we propose a novel method called Adversarial and
Contrastive Variational Autoencoder (ACVAE) for sequential recommendation.
Specifically, we first introduce the adversarial training for sequence
generation under the Adversarial Variational Bayes (AVB) framework, which
enables our model to generate high-quality latent variables. Then, we employ
the contrastive loss. The latent variables will be able to learn more
personalized and salient characteristics by minimizing the contrastive loss.
Besides, when encoding the sequence, we apply a recurrent and convolutional
structure to capture global and local relationships in the sequence. Finally,
we conduct extensive experiments on four real-world datasets. The experimental
results show that our proposed ACVAE model outperforms other state-of-the-art
methods.
