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

TL;DR

This paper introduces RDiffBR, a residual diffusion framework that enhances bundle recommendation models by adapting to item-level variability, significantly improving performance with minimal additional training time.

Contribution

It proposes a novel residual diffusion approach to address item-level variability in bundle recommendation, improving model robustness and effectiveness.

Findings

RDiffBR improves Recall and NDCG by up to 23%.

It enhances model adaptability to item-level changes.

Training time increases by only about 4%.

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%.