利用共现用户信息增强事件序列表示，提升模型性能。
Looking around you: external information enhances representations for event sequences
- 通过聚合多用户表示，引入共时上下文信息。
- 可学习注意力机制在9个数据集上显著提升指标表现。
- 适用于金融、电商等快速变化场景的序列建模任务。
表示学习在购物、客户交易及人类行为等不同领域中广泛应用。然而，现有事件序列模型通常孤立处理每个序列，忽略了同时发生序列间的上下文信息。这一局限在金融、电商等快速变化领域或某些序列缺乏近期事件的情况下尤为严重。本文提出一种方法，通过聚合多个用户表示，在多序列共现场景下增强特定用户的表示能力，优于独立处理各序列的效果。研究探索了从简单平均池化到可学习注意力等多种聚合方式，能捕捉更复杂的用户间信息流动。所提方法基于现有编码器构建，支持高效微调。在九个不同领域的事件序列数据集（包括金融、电商、娱乐等）和下游任务中，可学习注意力在有无微调情况下均提升评估指标，平均池化也带来小但显著的改进。
原文摘要 · Abstract (English)
Representation learning produces models in different domains, such as store purchases, client transactions, and general people's behavior. However, such models for event sequences usually process each sequence in isolation, ignoring context from those that co-occur in time. This limitation is particularly problematic in domains with fast-evolving conditions, like finance and e-commerce, or when certain sequences lack recent events. We develop a method that aggregates information from multiple user representations, augmenting a specific user's representation in a setting with multiple co-occurring event sequences, achieving better quality than processing each sequence independently. Our study considers diverse aggregation approaches, ranging from simple pooling techniques to Learnable attention aggregation, that can highlight more complex information flow among other users. The proposed methods operate on top of an existing encoder and support its efficient fine-tuning. Across nine diverse event sequence datasets (finance, e-commerce, entertainment, etc.) and downstream tasks, Learnable attention improves metric scores, both with and without fine-tuning, while mean pooling yields a smaller but still significant gain.
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