TRACE：基于Transformer的带属性点击流事件序列的用户表示
TRACE: Transformer-based user Representations from Attributed Clickstream Event sequences
AI总结：
提出TRACE模型，利用Transformer从多会话点击流生成用户嵌入，通过多任务学习捕获长期偏好，在旅游电商数据集上表现优于传统架构。
摘要：
对于浏览旅游电商网站的用户，研究和购买产品的过程通常会在较长时间内跨越多个会话，产生复杂的浏览模式。由此产生的点击流数据记录了这些用户旅程，为提取能显著增强个性化推荐的洞察提供了宝贵机会。我们提出了TRACE，一种新颖的基于Transformer的方法，专为从实时多会话点击流中生成丰富的用户嵌入以用于实时推荐应用而设计。先前的工作主要关注单会话产品序列，而TRACE利用跨越多个用户会话的全站页面浏览序列来建模长期参与度。采用多任务学习框架，TRACE捕获综合的用户偏好和意图，并将其提炼为低维表示。我们在包含真实用户旅程的大规模旅游电商数据集上进行了广泛实验，结果表明TRACE在长页面历史和稀疏目标挑战尤为突出的情况下，性能优于原始Transformer和LLM风格架构。学习到的嵌入的可视化揭示了与潜在用户状态和行为相对应的有意义的聚类，突显了TRACE通过捕获细致的用户交互和偏好来增强推荐系统的潜力。
英文摘要：
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
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。
