Interactive Sequential Basket Recommendation by Learning Basket Couplings and Positive/Negative Feedback

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-/interbasket 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.

Automated summary

The article introduces a new interactive sequential basket recommendation setting, which iteratively predicts next baskets by learning the couplings between intra-/inter-basket items and positive/negative user feedback. The hierarchical attentive encoder-decoder model (HAEM) continuously recommends next baskets by analyzing item relations within and between baskets, and incorporating user feedback to refine NBRS. Empirical analysis shows that HAEM significantly outperforms existing baselines for accurate and novel recommendation.