Discrete Diffusion for Bundle Construction

As a central task in product bundling, bundle construction aims to select a subset of items from huge item catalogs to complete a partial bundle. Existing methods often rely on the sequential construction paradigm that predicts items one at a time, nevertheless, this paradigm is fundamentally unsuitable for the essentially unordered bundles. In contrast, the non-sequential construction paradigm models bundle as a set, while it still faces two dimensionality curses: the combination complexity is exponential to the catalog size and bundle length. Accordingly, we identify two technical challenges: 1) how to effectively and efficiently model the higher-order intra-bundle relations with the growth of bundle length; and 2) how to learn item embeddings that are sufficiently discriminative while maintaining a relatively smaller search space other than the huge item set. To address these challenges, we propose DDBC, a Discrete Diffusion model for Bundle Construction. DDBC leverages a masked denoising diffusion process to build bundles non-sequentially, capturing joint dependencies among items without relying on certain pre-defined order. To mitigate the curse of large catalog size, we integrate residual vector quantization (RVQ), which compresses item embeddings into discrete codes drawn from a globally shared codebook, enabling more efficient search while retaining semantic granularity. We evaluate our method on real-world bundle construction datasets of music playlist continuation and fashion outfit completion, and the experimental results show that DDBC can achieve more than 100\% relative performance improvements compared with state-of-the-art baseline methods. Ablation and model analyses further confirm the effectiveness of both the diffusion backbone and RVQ tokenizer, where the performance gain is more significant for larger catalog size and longer bundle length. Our code is available at https://anonymous.4open.science/r/DDBC-44EE.