Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks

• URL: http://arxiv.org/abs/2510.11903v2
• Date: Thu, 06 Nov 2025 03:12:17 GMT
• Title: Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks
• Authors: Rizal Fathony, Igor Melnyk, Owen Reinert, Nam H. Nguyen, Daniele Rosa, C. Bayan Bruss,
• Abstract summary: User event modeling plays a central role in many machine learning applications, with use cases spanning e-commerce, social media, finance, cybersecurity, and other domains.<n>These two types of events are typically modeled separately, using sequence-based methods for personal events and graph-based methods for relational events.<n>Despite the need to capture both event types in real-world systems, prior work has rarely considered them together.<n>This is often due to the convenient simplification that user behavior can be adequately represented by a single formalization, either as a sequence or a graph.
• Score: 9.362679306539183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: User event modeling plays a central role in many machine learning applications, with use cases spanning e-commerce, social media, finance, cybersecurity, and other domains. User events can be broadly categorized into personal events, which involve individual actions, and relational events, which involve interactions between two users. These two types of events are typically modeled separately, using sequence-based methods for personal events and graph-based methods for relational events. Despite the need to capture both event types in real-world systems, prior work has rarely considered them together. This is often due to the convenient simplification that user behavior can be adequately represented by a single formalization, either as a sequence or a graph. To address this gap, there is a need for public datasets and prediction tasks that explicitly incorporate both personal and relational events. In this work, we introduce a collection of such datasets, propose a unified formalization, and empirically show that models benefit from incorporating both event types. Our results also indicate that current methods leave a notable room for improvements. We release these resources to support further research in unified user event modeling and encourage progress in this direction.

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