Sequential recommendation , such as next-basket recommender systems (NBRS), which model users’ sequential behaviors and the relevant context/session, has recently attracted much attention from the research community. Existing session-based NBRS involve session representation and inter-basket relations but ignore their hybrid couplings with the intra-basket items, often producing irrelevant or similar items in the next basket. In addition, they do not predict next-baskets (more than one next basket recommended). Interactive recommendation further involves user feedback on the recommended basket. The existing work on next-item recommendation involves positive feedback on selected items but ignores negative feedback on unselected ones. Here, we introduce a new setting— interactive sequential basket recommendation , which iteratively predicts next baskets by learning the intra-/inter-basket couplings between items and both positive and negative user feedback on recommended baskets. A hierarchical attentive encoder-decoder model (HAEM) continuously recommends next baskets one after another during sequential interactions with users after analyzing the item relations both within a basket and between adjacent sequential baskets (i.e., intra-/inter-basket couplings) and incorporating the user selection and unselection (i.e., positive/negative) feedback on the recommended baskets to refine NBRS. HAEM comprises a basket encoder and a sequence decoder to model intra-/inter-basket couplings and a prediction decoder to sequentially predict next-baskets by interactive feedback-based refinement. Empirical analysis shows that HAEM significantly outperforms the state-of-the-art baselines for NBRS and session-based recommenders for accurate and novel recommendation. We also show the effect of continuously refining sequential basket recommendation by including unselection feedback during interactive recommendation.

中文翻译：

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通过学习篮子耦合和正/负反馈的交互式顺序篮子推荐

顺序推荐， 如下一篮子推荐系统（NBRS），它模拟用户的顺序行为和相关的上下文/会话，最近引起了研究界的广泛关注。现存的基于会话的 NBRS涉及会话表示和篮子间关系，但忽略它们与篮子内项目的混合耦合，通常会在下一个篮子中产生不相关或相似的项目。此外，他们不预测下一篮子（推荐超过一个下一篮子）。互动推荐进一步涉及用户对推荐篮子的反馈。关于下一个项目推荐的现有工作涉及对选定项目的积极反馈，但忽略对未选择项目的负面反馈。在这里，我们介绍一个新的设置——交互式顺序购物篮推荐，它通过学习物品之间的篮内/篮间耦合以及对推荐篮子的正面和负面用户反馈来迭代地预测下一个篮子。一种分层注意编码器-解码器模型（HAEM）在分析篮子内和相邻顺序篮子之间的项目关系（即篮子内/篮子间耦合）并结合用户选择和取消选择（即，对推荐篮子的正面/负面反馈，以完善 NBRS。HAEM 包括一个篮子编码器和一个序列解码器，用于对篮子内/篮子间耦合进行建模，以及一个预测解码器，通过基于交互式反馈的细化顺序预测下一个篮子。实证分析表明，HAEM 在准确和新颖的推荐方面明显优于 NBRS 和基于会话的推荐器的最新基线。