Bridging User Dynamics: Transforming Sequential Recommendations with Schr\"odinger Bridge and Diffusion Models

TL;DR

This paper introduces a novel sequential recommendation model that integrates Schr"odinger Bridge and diffusion models to better incorporate user-specific information and collaborative data, improving recommendation accuracy.

Contribution

It proposes the SdifRec model replacing Gaussian priors with user states and extends it to con-SdifRec using user clustering, advancing diffusion-based recommendation methods.

Findings

SdifRec outperforms existing models on benchmark datasets.

Con-SdifRec effectively leverages user clustering for improved recommendations.

Models demonstrate robustness and efficiency in extensive experiments.

Abstract

Sequential recommendation has attracted increasing attention due to its ability to accurately capture the dynamic changes in user interests. We have noticed that generative models, especially diffusion models, which have achieved significant results in fields like image and audio, hold considerable promise in the field of sequential recommendation. However, existing sequential recommendation methods based on diffusion models are constrained by a prior distribution limited to Gaussian distribution, hindering the possibility of introducing user-specific information for each recommendation and leading to information loss. To address these issues, we introduce the Schr\"odinger Bridge into diffusion-based sequential recommendation models, creating the SdifRec model. This allows us to replace the Gaussian prior of the diffusion model with the user's current state, directly modeling the process from a user's current state to the target recommendation. Additionally, to better utilize collaborative information in recommendations, we propose an extended version of SdifRec called con-SdifRec, which utilizes user clustering information as a guiding condition to further enhance the posterior distribution. Finally, extensive experiments on multiple public benchmark datasets have demonstrated the effectiveness of SdifRec and con-SdifRec through comparison with several state-of-the-art methods. Further in-depth analysis has validated their efficiency and robustness.