Modeling Item-Level Dynamic Variability with Residual Diffusion for Bundle Recommendation

TL;DR

This work tackles item-level dynamic variability in bundle-item affiliations that challenge traditional bundle recommendation. It introduces RDiffBR, a model-agnostic, generative framework that applies a residual diffusion process to item-level bundle embeddings produced by a BR model, using a forward diffusion to inject noise and a residual-denoising reverse process to recover embeddings aligned with bundle themes under $Z^*=Z\\cup\\Delta^+\\setminus\\Delta^-$. The approach is trained jointly with the BR backbone via a composite loss $\,\mathcal{L}=\mathcal{L}_{BR-model}+\\lambda\mathcal{L}_{diff}$ and evaluated on six backbones across four datasets, showing up to $\\approx 23\\%$ gains in Recall and NDCG, and only a modest $\\approx 4\\%$ increase in training time. Key contributions include a practical residual diffusion module that enhances item-level embedding generation without altering existing architectures, extensive empirical validation under varying B-I variability levels, and insights into hyperparameter effects and the importance of the residual pathway. Overall, RDiffBR provides a scalable, plug-in solution to improve BR performance in realistic, fluctuating B-I scenarios with meaningful implications for deployment in production systems.

Abstract

Existing solutions for bundle recommendation (BR) have achieved remarkable effectiveness for predicting the user's preference for prebuilt bundles. However, bundle-item (B-I) affiliation will vary dynamically in real scenarios. For example, a bundle themed as 'casual outfit' may add 'hat' or remove 'watch' due to factors such as seasonal variations, changes in user preferences or inventory adjustments. Our empirical study demonstrates that the performance of mainstream BR models may fluctuate or decline under item-level variability. This paper makes the first attempt to address the above problem and proposes a novel Residual Diffusion for Bundle Recommendation(RDiffBR)asamodel-agnostic generative framework which can assist a BR model in adapting this scenario. During the initial training of the BR model, RDiffBR employs a residual diffusion model to process the item-level bundle embeddings which are generated by the BR model to represent bundle theme via a forward-reverse process. In the inference stage, RDiffBR reverses item-level bundle embeddings obtained by the well-trained bundle model under B-I variability scenarios to generate the effective item level bundle embeddings. In particular, the residual connection in our residual approximator significantly enhances BR models' ability to generate high-quality item-level bundle embeddings. Experiments on six BR models and four public datasets from different domains show that RDiffBR improves the performance of Recall and NDCG of backbone BR models by up to 23%, while only increases training time about 4%.

Figures (8)

• Figure 1: An example showing that bundle-item affiliation is changed while bundle theme remains relatively stable.
• Figure 2: Overview of the proposed RDiffBR framework.
• Figure 3: The processing of the B-I affiliation to match the scenario of B-I dynamic variability.
• Figure 4: Recall@40 and NDCG@40 performance comparisons of the backbone models and our RDiffBR under different levels of B-I variations in terms of Recall@40 and NDCG@40. The performance of K$= \{10, 20, 80\}$ is consistent with K$= 40$.
• Figure 5: Blue is distribution $E^{IL}_B$ under $\rho=0$. Red is distribution $E^{IL}_B$ under $\rho=-3$.