Problem
Research questions and friction points this paper is trying to address.
Modeling inter-temporal context in human actions
Embedding actions and time intervals jointly
Understanding human behavior on digital platforms
Characterizing Human Actions in the Digital Platform by Temporal Context
🤖 AI Summary
Existing behavioral modeling approaches treat user actions as discrete event sequences, neglecting the contextual information embedded in inter-action time intervals—leading to incomplete behavioral understanding and poor interpretability. To address this, we propose the dual-scale Action-Timing Context (ATC) framework, the first to systematically model *inter-action temporal context* by jointly embedding action types and time intervals within a unified representation space, thereby capturing fine-grained temporal structure. ATC employs dual-scale temporal embedding and low-dimensional action representation learning to yield interpretable and consistent behavioral embeddings. Extensive experiments on multiple real-world digital platform log datasets demonstrate that ATC significantly improves performance in behavioral prediction, post-hoc interpretability, and analysis of sociological mechanisms—including knowledge accumulation and information diffusion—thereby filling a critical gap in temporal structural modeling of human behavior.
📝 Abstract
Recent advances in digital platforms generate rich, high-dimensional logs of human behavior, and machine learning models have helped social scientists explain knowledge accumulation, communication, and information diffusion. Such models, however, almost always treat behavior as sequences of actions, abstracting the inter-temporal information among actions. To close this gap, we introduce a two-scale Action-Timing Context(ATC) framework that jointly embeds each action and its time interval. ATC obtains low-dimensional representations of actions and characterizes them with inter-temporal information. We provide three applications of ATC to real-world datasets and demonstrate that the method offers a unified view of human behavior. The presented qualitative findings demonstrate that explicitly modeling inter-temporal context is essential for a comprehensive, interpretable understanding of human activity on digital platforms.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Two-scale Action-Timing Context framework
Embeds actions and time intervals jointly
Models inter-temporal context explicitly
🔎 Similar Papers
No similar papers found.
