From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation

TL;DR

This paper introduces FatsMB, a diffusion-based framework for multi-behavior sequential recommendation that models user preferences in latent space, enabling diverse and accurate predictions by transferring from behavior-agnostic to behavior-specific preferences.

Contribution

The paper proposes a novel diffusion model framework with a multi-behavior autoencoder and behavior-specific transfer in latent space for improved recommendation accuracy and diversity.

Findings

FatsMB outperforms existing methods on real-world datasets.

The model effectively captures user preferences across multiple behaviors.

It provides diverse recommendations with higher accuracy.

Abstract

Multi-behavior sequential recommendation (MBSR) aims to learn the dynamic and heterogeneous interactions of users' multi-behavior sequences, so as to capture user preferences under target behavior for the next interacted item prediction. Unlike previous methods that adopt unidirectional modeling by mapping auxiliary behaviors to target behavior, recent concerns are shifting from behavior-fixed to behavior-specific recommendation. However, these methods still ignore the user's latent preference that underlying decision-making, leading to suboptimal solutions. Meanwhile, due to the asymmetric deterministic between items and behaviors, discriminative paradigm based on preference scoring is unsuitable to capture the uncertainty from low-entropy behaviors to high-entropy items, failing to provide efficient and diverse recommendation. To address these challenges, we propose \textbf{FatsMB}, a framework based diffusion model that guides preference generation \textit{\textbf{F}rom Behavior-\textbf{A}gnostic \textbf{T}o Behavior-\textbf{S}pecific} in latent spaces, enabling diverse and accurate \textit{\textbf{M}ulti-\textbf{B}ehavior Sequential Recommendation}. Specifically, we design a Multi-Behavior AutoEncoder (MBAE) to construct a unified user latent preference space, facilitating interaction and collaboration across Behaviors, within Behavior-aware RoPE (BaRoPE) employed for multiple information fusion. Subsequently, we conduct target behavior-specific preference transfer in the latent space, enriching with informative priors. A Multi-Condition Guided Layer Normalization (MCGLN) is introduced for the denoising. Extensive experiments on real-world datasets demonstrate the effectiveness of our model.