How to model Human Actions distribution with Event Sequence Data

2025-10-07

AI Summary

This paper addresses the problem of predicting the distribution of future events in human action sequences—emphasizing the composition of likely outcomes rather than precise temporal ordering—a task critical for applications in retail, finance, healthcare, and recommender systems. To overcome the limitations of dominant autoregressive paradigms—namely, their strong sequential dependency and tendency toward category-mode collapse—we propose a non-autoregressive distributional prediction framework. Our approach introduces KL divergence to quantify temporal distributional drift, identifies distributional imbalance as the primary cause of mode collapse, and incorporates three key components: an explicit distribution-aware learning objective, locally order-invariant representations, and multi-token parallel prediction. Experiments across multiple real-world datasets demonstrate substantial improvements over state-of-the-art baselines. The framework offers both interpretability and practicality, providing a principled alternative for behavioral sequence modeling with clear design rationales and deployable mechanisms.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Reasoning under Uncertainty: Sequential Decision Making
• Machine Learning: Multimodal Learning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This paper studies forecasting of the future distribution of events in human action sequences, a task essential in domains like retail, finance, healthcare, and recommendation systems where the precise temporal order is often less critical than the set of outcomes. We challenge the dominant autoregressive paradigm and investigate whether explicitly modeling the future distribution or order-invariant multi-token approaches outperform order-preserving methods. We analyze local order invariance and introduce a KL-based metric to quantify temporal drift. We find that a simple explicit distribution forecasting objective consistently surpasses complex implicit baselines. We further demonstrate that mode collapse of predicted categories is primarily driven by distributional imbalance. This work provides a principled framework for selecting modeling strategies and offers practical guidance for building more accurate and robust forecasting systems.

Problem

• Forecasting future distribution of human action sequences
• Challenging autoregressive paradigm for event prediction
• Addressing mode collapse due to distributional imbalance

Innovation

• Explicit distribution forecasting surpasses implicit baselines
• KL-based metric quantifies temporal drift in sequences
• Framework addresses distributional imbalance and mode collapse