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

2025-10-13

AI Summary

Existing user event modeling approaches treat individual behavioral sequences and relational interactions (e.g., social graphs) separately, failing to capture their synergistic effects in a unified framework. Method: We introduce the first publicly available dataset and standardized prediction task supporting *joint modeling* of individual and relational events. We propose a unified formalism that integrates temporal sequence modeling with graph neural networks to jointly represent dynamic behavioral sequences and static/dynamic social structures. Contribution/Results: Experiments demonstrate substantial improvements in event prediction performance: our method outperforms unimodal baselines by an average of 12.7% across multiple metrics. To foster reproducibility and community advancement, we release the dataset, source code, and benchmark tasks—establishing a new paradigm and foundational infrastructure for modeling complex, socially embedded user behavior.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Integrating sequential and relational user event modeling
• Addressing lack of unified datasets for both event types
• Developing models combining personal and relational event prediction

Innovation

• Combining sequence and graph methods for events
• Creating datasets with both personal and relational events
• Proposing unified formalization for user event modeling