TRACE: Transformer-based user Representations from Attributed Clickstream Event sequences

• URL: http://arxiv.org/abs/2409.12972v1
• Date: Mon, 2 Sep 2024 23:33:19 GMT
• Title: TRACE: Transformer-based user Representations from Attributed Clickstream Event sequences
• Authors: William Black, Alexander Manlove, Jack Pennington, Andrea Marchini, Ercument Ilhan, Vilda Markeviciute,
• Abstract summary: We introduce TRACE, a novel approach to generate rich user embeddings from live multi-session clickstreams for real-time recommendation applications. We demonstrate TRACE's superior performance over vanilla transformer and LLM-style architectures through extensive experiments on a large-scale travel e-commerce dataset.
• Score: 37.69303106863453
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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