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

Published: 23 Oct 2025, Last Modified: 23 Oct 2025LOG 2025 Conference - NeurIPS Fast Track PosterEveryoneRevisionsBibTeXCC BY 4.0
Keywords: User Events, Graph, Sequence, GNN, Transformers, Datasets
TL;DR: Note: the paper was submitted to NeurIPS 2025 Datasets & Benchmarks Track, not the regular track
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.
Supplementary Materials: zip
Neurips Id: 705
Neurips Title: Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks
Neurips Authors: Rizal Fathony, Igor Melnyk, Owen Reinert, Nam H Nguyen, Daniele Rosa, C. Bayan Bruss
Neurips Pdf: pdf
Neurips Review Document: pdf
Changes Made: pdf
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Submission Number: 3
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