Addressing Missing Data Issue for Diffusion-based Recommendation

TL;DR

This paper introduces TDM, a novel diffusion model that uses dual-side Thompson sampling to simulate and handle missing data in user sequences, improving recommendation robustness and efficiency.

Contribution

The paper proposes a dual-side Thompson sampling-based diffusion model that effectively manages missing data in sequential recommendation tasks, a novel approach in this domain.

Findings

TDM outperforms existing models in handling missing data.

TDM improves recommendation accuracy and robustness.

Theoretical analysis confirms the effectiveness of the approach.

Abstract

Diffusion models have shown significant potential in generating oracle items that best match user preference with guidance from user historical interaction sequences. However, the quality of guidance is often compromised by unpredictable missing data in observed sequence, leading to suboptimal item generation. Since missing data is uncertain in both occurrence and content, recovering it is impractical and may introduce additional errors. To tackle this challenge, we propose a novel dual-side Thompson sampling-based Diffusion Model (TDM), which simulates extra missing data in the guidance signals and allows diffusion models to handle existing missing data through extrapolation. To preserve user preference evolution in sequences despite extra missing data, we introduce Dual-side Thompson Sampling to implement simulation with two probability models, sampling by exploiting user preference from both item continuity and sequence stability. TDM strategically removes items from sequences based on dual-side Thompson sampling and treats these edited sequences as guidance for diffusion models, enhancing models' robustness to missing data through consistency regularization. Additionally, to enhance the generation efficiency, TDM is implemented under the denoising diffusion implicit models to accelerate the reverse process. Extensive experiments and theoretical analysis validate the effectiveness of TDM in addressing missing data in sequential recommendations.