用多会话点击流数据生成用户嵌入，提升旅行电商实时推荐效果
TRACE: Transformer-based user Representations from Attributed Clickstream Event sequences
- 基于Transformer建模跨会话点击序列，捕捉长期用户行为
- 在大规模旅行电商数据上优于传统模型，显著提升推荐精度
- 适合需要理解用户长期偏好的推荐系统研发人员
用户在旅游电商平台浏览和购买商品时，常形成跨越多个会话的复杂浏览路径，产生的点击流数据记录了完整的用户旅程，为个性化推荐提供了宝贵线索。本文提出TRACE，一种专用于生成实时推荐用丰富用户嵌入的新型Transformer方法，利用跨会话的全站页面浏览序列建模长期用户参与度。采用多任务学习框架，将用户偏好与意图提炼为低维表示。在大规模真实旅行电商数据集上的实验表明，面对长页面历史和稀疏目标等挑战，TRACE在性能上显著优于基础Transformer和大型语言模型风格架构。嵌入可视化显示了对应潜在用户状态和行为的有意义聚类，验证其在捕捉细微用户交互与偏好方面的潜力。
原文摘要 · Abstract (English)
For users navigating travel e-commerce websites, the process of researching products and making a purchase often results in intricate browsing patterns that span numerous sessions over an extended period of time. The resulting clickstream data chronicle these user journeys and present valuable opportunities to derive insights that can significantly enhance personalized recommendations. We introduce TRACE, a novel transformer-based approach tailored to generate rich user embeddings from live multi-session clickstreams for real-time recommendation applications. Prior works largely focus on single-session product sequences, whereas TRACE leverages site-wide page view sequences spanning multiple user sessions to model long-term engagement. Employing a multi-task learning framework, TRACE captures comprehensive user preferences and intents distilled into low-dimensional representations. We demonstrate TRACE's superior performance over vanilla transformer and LLM-style architectures through extensive experiments on a large-scale travel e-commerce dataset of real user journeys, where the challenges of long page-histories and sparse targets are particularly prevalent. Visualizations of the learned embeddings reveal meaningful clusters corresponding to latent user states and behaviors, highlighting TRACE's potential to enhance recommendation systems by capturing nuanced user interactions and preferences
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