# Enhancing Item-level Bundle Representation for Bundle Recommendation

arXiv: https://arxiv.org/abs/2311.16892 · HTML: https://mlsanity.com/paper/2311.16892/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2023.

## Built on by (most cited first, 3 of 3)

- [Bundle Recommendation with Item-level Causation-enhanced Multi-view Learning](https://mlsanity.com/paper/2408.08906/) (2024)
- [A Reproducibility Study of Product-side Fairness in Bundle Recommendation](https://mlsanity.com/paper/2507.14352/) (2025)
- [Dual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model](https://mlsanity.com/paper/2604.14030/) (2026)

## Abstract (arXiv)

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.