用于用户行为轨迹预测的列表偏好扩散优化
Listwise Preference Diffusion Optimization for User Behavior Trajectories Prediction
AI总结：
针对多步用户行为轨迹预测中难以捕捉全局列表依赖的问题，提出基于扩散的列表偏好扩散优化（LPDO）框架，结合 Plackett-Luce 监督信号优化序列偏好，并引入 SeqMatch 等指标，在真实数据集上超越基线建立新基准。
摘要：
预测多步用户行为轨迹需要对跨未来动作的结构化偏好进行推理，这是传统序列推荐所忽视的挑战。该问题对于个性化电商和自适应内容分发等应用至关重要，在这些应用中，预测用户的完整动作序列能同时提升满意度和商业收益。我们指出了现有范式的一个关键局限：无法捕捉序列项目间的全局列表依赖关系。为解决此问题，我们将用户行为轨迹预测（UBTP）设定为一项明确建模长期用户偏好的新任务。我们引入了列表偏好扩散优化（LPDO），这是一种基于扩散的训练框架，可直接优化整个项目序列上的结构化偏好。LPDO 结合了 Plackett-Luce 监督信号，并推导出与列表排序似然对齐的紧致变分下界，从而在去噪步骤间实现连贯的偏好生成，并克服了先前扩散方法的独立 token 假设。为严格评估多步预测质量，我们提出了特定任务指标 Sequential Match (SeqMatch) 以衡量精确的轨迹一致性，并采用 Perplexity (PPL) 评估概率保真度。在真实用户行为基准上的大量实验表明，LPDO 始终优于最先进的基线，为基于扩散模型的结构化偏好学习确立了新基准。
英文摘要：
Forecasting multi-step user behavior trajectories requires reasoning over structured preferences across future actions, a challenge overlooked by traditional sequential recommendation. This problem is critical for applications such as personalized commerce and adaptive content delivery, where anticipating a user's complete action sequence enhances both satisfaction and business outcomes. We identify an essential limitation of existing paradigms: their inability to capture global, listwise dependencies among sequence items. To address this, we formulate User Behavior Trajectory Prediction (UBTP) as a new task setting that explicitly models long-term user preferences. We introduce Listwise Preference Diffusion Optimization (LPDO), a diffusion-based training framework that directly optimizes structured preferences over entire item sequences. LPDO incorporates a Plackett-Luce supervision signal and derives a tight variational lower bound aligned with listwise ranking likelihoods, enabling coherent preference generation across denoising steps and overcoming the independent-token assumption of prior diffusion methods. To rigorously evaluate multi-step prediction quality, we propose the task-specific metric Sequential Match (SeqMatch), which measures exact trajectory agreement, and adopt Perplexity (PPL), which assesses probabilistic fidelity. Extensive experiments on real-world user behavior benchmarks demonstrate that LPDO consistently outperforms state-of-the-art baselines, establishing a new benchmark for structured preference learning with diffusion models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
