  Next-basket recommendation (NBR) aims to infer the items in the next basket
given the corresponding basket sequence. Existing NBR methods are mainly based
on either message passing in a plain graph or transition modelling in a basket
sequence. However, these methods only consider point-to-point binary item
relations while item dependencies in real world scenarios are often in higher
order. Additionally, the importance of the same item to different users varies
due to variation of user preferences, and the relations between items usually
involve various aspects. As pretrained language models (PLMs) excel in multiple
tasks in natural language processing (NLP) and computer vision (CV), many
researchers have made great efforts in utilizing PLMs to boost recommendation.
However, existing PLM-based recommendation methods degrade when encountering
Out-Of-Vocabulary (OOV) items. OOV items are those whose IDs are out of PLM's
vocabulary and thus unintelligible to PLM. To settle the above challenges, we
propose a novel method HEKP4NBR, which transforms the knowledge graph (KG) into
prompts, namely Knowledge Tree Prompt (KTP), to help PLM encode the OOV item
IDs in the user's basket sequence. A hypergraph convolutional module is
designed to build a hypergraph based on item similarities measured by an MoE
model from multiple aspects and then employ convolution on the hypergraph to
model correlations among multiple items. Extensive experiments are conducted on
HEKP4NBR on two datasets based on real company data and validate its
effectiveness against multiple state-of-the-art methods.
