The context information such as product category plays a critical role in sequential recommendation. Recent years have witnessed a growing interest in context-aware sequential recommender systems. Existing studies often treat the contexts as auxiliary feature vectors without considering the sequential dependency in contexts. However, such a dependency provides valuable clues to predict the user's future behavior. For example, a user might buy electronic accessories after he/she buy an electronic product. In this paper, we propose a novel seq2seq translation architecture to highlight the importance of sequential dependency in contexts for sequential recommendation. Specifically, we first construct a collateral context sequence in addition to the main interaction sequence. We then generalize recent advancements in translation model from sequences of words in two languages to sequences of items and contexts in recommender systems. Taking the category information as an item's context, we develop a basic coupled and an extended tripled seq2seq translation models to encode the category-item and item-category-item relations between the item and context sequences. We conduct extensive experiments on three real world datasets. The results demonstrate the superior performance of the proposed model compared with the state-of-the-art baselines.

中文翻译：

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用于顺序推荐的 Seq2seq 翻译模型

产品类别等上下文信息在顺序推荐中起着至关重要的作用。近年来，人们对上下文感知的顺序推荐系统越来越感兴趣。现有研究通常将上下文视为辅助特征向量，而不考虑上下文中的顺序依赖性。然而，这种依赖为预测用户未来的行为提供了有价值的线索。例如，用户可能会在购买电子产品后购买电子配件。在本文中，我们提出了一种新颖的 seq2seq 翻译架构，以强调顺序依赖在上下文中对顺序推荐的重要性。具体来说，除了主要交互序列之外，我们首先构建了一个附属上下文序列。然后，我们将翻译模型的最新进展从两种语言的单词序列推广到推荐系统中的项目和上下文序列。将类别信息作为项目的上下文，我们开发了一个基本的耦合和扩展的三重 seq2seq 翻译模型来编码项目和上下文序列之间的类别-项目和项目-类别-项目关系。我们对三个真实世界的数据集进行了广泛的实验。结果表明，与最先进的基线相比，所提出的模型具有优越的性能。我们开发了一个基本的耦合和扩展的三重 seq2seq 翻译模型来编码项目和上下文序列之间的类别-项目和项目-类别-项目关系。我们对三个真实世界的数据集进行了广泛的实验。结果表明，与最先进的基线相比，所提出的模型具有优越的性能。我们开发了一个基本的耦合和扩展的三重 seq2seq 翻译模型来编码项目和上下文序列之间的类别-项目和项目-类别-项目关系。我们对三个真实世界的数据集进行了广泛的实验。结果表明，与最先进的基线相比，所提出的模型具有优越的性能。