  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.
