发表机构
HSE University（俄罗斯高等经济研究大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出结合对比表示学习与状态空间模型的混合方法用于交易序列用户中心建模，探讨两种整合策略，在三个数据集实验中混合模型比单独模型有改进且收敛更快，可解释性分析还能揭示事件过滤及关键交易特征。
AI 中文摘要
我们提出了一种用于交易事件序列用户中心建模的混合方法，将对比表示学习（CoLES）与状态空间模型（SSM）相结合。对比方法能产生高质量的压缩用户表示，但现有编码器——循环神经网络（RNN）和变换器（Transformer）——分别存在梯度消失或二次复杂度问题。选择性SSM曼巴（Mamba）能有效处理长程依赖，但在个性化用户分析方面研究不足。我们研究了两种整合策略：用CoLES嵌入初始化曼巴隐藏状态；将投影后的CoLES嵌入作为前缀令牌添加到输入序列。在三个公共数据集上的实验表明，混合模型比单独的曼巴和CoLES与线性分类器有持续改进，收敛速度快2至3倍。通过离散化步长图和集成梯度进行的可解释性分析揭示了在行为丰富数据集上的选择性事件过滤，并识别出最具信息量交易特征。
英文摘要
We propose a hybrid approach for user-centric modeling of transactional event sequences that combines contrastive representation learning (CoLES) with State Space Models (SSMs). While contrastive methods yield high-quality compressed user representations, existing encoders -- RNNs and Transformers -- suffer from vanishing gradients or quadratic complexity, respectively. Mamba, a selective SSM, efficiently handles long-range dependencies but remains underexplored for personalized user analysis. We investigate two integration strategies: (1)~initializing the Mamba hidden state with a CoLES embedding, and (2)~prepending the projected CoLES embedding as a prefix token to the input sequence. Both approaches supply the model with an informative user prior from the first step. Experiments on three public datasets -- Age (multiclass age-group prediction), MBD (multi-label product acquisition), and Taobao (binary purchase prediction) -- demonstrate consistent improvements over standalone Mamba and CoLES with a linear classifier, with the hybrid models converging 2--3$\times$ faster than the plain SSM baseline. Explainability analysis via discretization-step maps and Integrated Gradients reveals selective event filtering on behavior-rich datasets and identifies the most informative transaction features.
