  Bundle recommendation approaches offer users a set of related items on a
particular topic. The current state-of-the-art (SOTA) method utilizes
contrastive learning to learn representations at both the bundle and item
levels. However, due to the inherent difference between the bundle-level and
item-level preferences, the item-level representations may not receive
sufficient information from the bundle affiliations to make accurate
predictions. In this paper, we propose a novel approach EBRec, short of
Enhanced Bundle Recommendation, which incorporates two enhanced modules to
explore inherent item-level bundle representations. First, we propose to
incorporate the bundle-user-item (B-U-I) high-order correlations to explore
more collaborative information, thus to enhance the previous bundle
representation that solely relies on the bundle-item affiliation information.
Second, we further enhance the B-U-I correlations by augmenting the observed
user-item interactions with interactions generated from pre-trained models,
thus improving the item-level bundle representations. We conduct extensive
experiments on three public datasets, and the results justify the effectiveness
of our approach as well as the two core modules. Codes and datasets are
available at https://github.com/answermycode/EBRec.
