  Product bundling has been a prevailing marketing strategy that is beneficial
in the online shopping scenario. Effective product bundling methods depend on
high-quality item representations, which need to capture both the individual
items' semantics and cross-item relations. However, previous item
representation learning methods, either feature fusion or graph learning,
suffer from inadequate cross-modal alignment and struggle to capture the
cross-item relations for cold-start items. Multimodal pre-train models could be
the potential solutions given their promising performance on various multimodal
downstream tasks. However, the cross-item relations have been under-explored in
the current multimodal pre-train models. To bridge this gap, we propose a novel
and simple framework Cross-Item Relational Pre-training (CIRP) for item
representation learning in product bundling. Specifically, we employ a
multimodal encoder to generate image and text representations. Then we leverage
both the cross-item contrastive loss (CIC) and individual item's image-text
contrastive loss (ITC) as the pre-train objectives. Our method seeks to
integrate cross-item relation modeling capability into the multimodal encoder,
while preserving the in-depth aligned multimodal semantics. Therefore, even for
cold-start items that have no relations, their representations are still
relation-aware. Furthermore, to eliminate the potential noise and reduce the
computational cost, we harness a relation pruning module to remove the noisy
and redundant relations. We apply the item representations extracted by CIRP to
the product bundling model ItemKNN, and experiments on three e-commerce
datasets demonstrate that CIRP outperforms various leading representation
learning methods.
