偏好漂移感知的子序列学习与层次化上下文融合用于长序列生成式推荐
Preference-Drift-Aware Subsequence Learning and Hierarchical Context Fusion for Long-Sequence Generative Recommendation
AI总结：
针对长序列生成式推荐中偏好漂移与噪声问题，提出偏好漂移感知子序列学习与层次化上下文融合方法，提升准确性与效率。
AI中文摘要：
长序列生成式推荐方法通过自回归方式对用户的交互序列进行建模，以生成下一个项目的表示。现有方法通常分为两类：高效的全序列建模和目标感知的上下文检索。我们的实验表明，随着序列长度的增加，前者会产生持续增长的计算成本，而其准确性提升会迅速饱和，甚至因噪声而下降；后者虽然缩短了输入序列，但容易受到语义一致但偏好不一致的噪声影响，并且可能面临上下文不完整的问题。这两种范式在处理历史信息时都忽略了用户偏好的动态变化以及跨子序列的依赖关系，从而限制了准确性和效率。为解决这些问题，我们提出了一种偏好漂移感知的子序列学习与层次化上下文融合方法，用于长序列生成式推荐。具体而言，我们利用多维偏好漂移信息学习可微分的软子序列边界，并通过带有软分配权重的线性注意力将每个子序列内的项目聚合为偏好一致的表示，从而避免了全序列注意力的开销。随后，采用交叉注意力机制来捕捉近期交互与相关子序列上下文之间的依赖关系，减轻学习近期项目表示时的噪声影响。最后，通过门控融合机制自适应地结合近期项目表示与全局子序列上下文，使得最终的目标表示能够同时编码近期和长期偏好。大量实验表明，我们的方法在推荐准确性和计算效率方面均持续优于现有基线方法。
英文摘要：
Long-sequence generative recommendation methods autoregressively model the user's interaction sequence to generate the next-item representation. Existing methods generally fall into two categories: efficient full-sequence modeling and target-aware context retrieval. Our experiments reveal that as the sequence length increases, the former incurs steadily growing computational cost while its accuracy gains quickly saturate and even degrade due to noise; the latter, though shortening the input sequence, is susceptible to noise that is semantically consistent yet preference-inconsistent, as well as to incomplete contexts. Both paradigms ignore the dynamic changes of user preferences and the cross-subsequence dependencies when handling historical information, thereby limiting accuracy and efficiency. To address these issues, we propose a preference-drift-aware subsequence learning and hierarchical context fusion for long-sequence generative recommendation. Specifically, we learn differentiable soft subsequence boundaries using multidimensional preference-drift information and aggregate items within each subsequence into preference-coherent representations via linear attention with soft assignment weights, thereby circumventing the expense of full-sequence attention. A cross-attention mechanism is then employed to capture dependencies between recent interactions and relevant subsequence contexts, mitigating noise in learning recent-item representations. Finally, a gated fusion mechanism adaptively combines the recent-item representation with the global subsequence context, allowing the resulting target representation to encode both recent and long-term preferences. Extensive experiments demonstrate that our method consistently outperforms existing baselines in both recommendation accuracy and computational efficiency.
