Build Your Own Bundle - A Neural Combinatorial Optimization Method
Qilin Deng, Kai Wang, Minghao Zhao, Runze Wu, Yu Ding, Zhene Zou, Yue Shang, Jianrong Tao, Changjie Fan
Abstract
In the business domain,bundling is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers. Existing recommender systems mostly focus on recommending individual items that users may be interested in, such as the considerable research work on collaborative filtering that directly models the interaction between users and items. In this paper, we target at a practical but less explored recommendation problem named personalized bundle composition, which aims to offer an optimal bundle (i.e., a combination of items) to the target user. To tackle this specific recommendation problem, we formalize it as a combinatorial optimization problem on a set of candidate items and solve it within a neural combinatorial optimization framework. Extensive experiments on public datasets are conducted to demonstrate the superiority of the proposed method.
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- Adaptive In-Context Learning with Large Language Models for Bundle GenerationZhu Sun, Kaidong Feng, Jie Yang, Xinghua Qu et al.SIGIR 2024 · 9 citations
- Fine-tuning Multimodal Large Language Models for Product BundlingXiaohao Liu, Jie Wu, Zhulin Tao, Yunshan Ma et al.KDD 2025 · 3 citations
- CIRP: Cross-Item Relational Pre-training for Multimodal Product BundlingYunshan Ma, Yingzhi He, Wenjun Zhong, Xiang Wang et al.ACM MM 2024 · 2 citations
- Interactive Visualization Recommendation with Hier-SUCBSongwen Hu, Ryan A. Rossi, Tong Yu, Junda Wu et al.WWW 2025 · 2 citations
- Discrete Diffusion for Bundle ConstructionTeng Tu, Ai Li, Yunshan Ma, Shuo Xu et al.ICLR 2026
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