![[2602.23132] From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.23132] From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation
Summary
This paper presents FatsMB, a novel framework for Multi-Behavior Sequential Recommendation (MBSR) that enhances user preference modeling by transitioning from behavior-agnostic to behavior-specific recommendations.
Why It Matters
As recommendation systems evolve, understanding user preferences across multiple behaviors is crucial for improving accuracy and diversity in recommendations. This research addresses limitations in current models, potentially leading to better user experiences in various applications such as e-commerce and content platforms.
Key Takeaways
- FatsMB framework improves user preference modeling in MBSR.
- Introduces a Multi-Behavior AutoEncoder for unified latent preference space.
- Utilizes Behavior-aware RoPE for effective information fusion.
- Incorporates Multi-Condition Guided Layer Normalization for denoising.
- Demonstrates effectiveness through extensive real-world dataset experiments.
Computer Science > Information Retrieval arXiv:2602.23132 (cs) [Submitted on 26 Feb 2026] Title:From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation Authors:Ruochen Yang, Xiaodong Li, Jiawei Sheng, Jiangxia Cao, Xinkui Lin, Shen Wang, Shuang Yang, Zhaojie Liu, Tingwen Liu View a PDF of the paper titled From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation, by Ruochen Yang and 8 other authors View PDF HTML (experimental) 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 \text...
