Context information, such as product category, plays a vital role in sequential recommendations. Recently, there has been a growing interest in context-aware sequential recommender systems. However, in previous studies, contexts have often been treated as auxiliary information without the consideration of the inter-sequence dependency between the item sequence and the context sequence. Such a dependency provides valuable details for predicting a user’s future behavior. For example, a user may buy electronic accessories after buying an electronic product. In this paper, we propose a context-aware seq2seq translation model to capture the inter-sequence dependency for sequential recommendations. The key component in our model is a tripled seq2seq translation architecture with an injected variational autoencoder (VAE). The tripled architecture, consisting of forward and backward translation, naturally encodes bi-directional inter-sequence dependency. Moreover, the injected VAE enables the translation process to redress the semantic imbalance between context and item. We conduct extensive experiments on four real-world datasets. The results show the superior performance of our model over the state-of-the-art baselines. The code and datasets are available at https://github.com/NLPWM-WHU/CAST.

上下文信息（例如产品类别）在顺序推荐中起着至关重要的作用。最近，人们对上下文感知的顺序推荐系统越来越感兴趣。然而，在以往的研究中，上下文往往被视为辅助信息，而没有考虑项目序列和上下文序列之间的序列间依赖关系. 这种依赖为预测用户的未来行为提供了有价值的细节。例如，用户可以在购买电子产品后购买电子配件。在本文中，我们提出了一个上下文感知的 seq2seq 翻译模型来捕获序列推荐的序列间依赖性。我们模型中的关键组件是带有注入变分自编码器 (VAE) 的三重 seq2seq 翻译架构。由前向和后向翻译组成的三重架构自然地编码了双向序列间依赖性。此外，注入的 VAE 使翻译过程能够纠正上下文和项目之间的语义不平衡。我们对四个真实世界的数据集进行了广泛的实验。结果表明我们的模型在最先进的基线上具有优越的性能。