Can We Predict the Next Question? A Collaborative Filtering Approach to Modeling User Behavior

2025-11-16

AI Summary

Existing conversational recommendation systems predominantly rely on static user modeling, failing to capture the dynamic evolution of user interests over interaction sequences. To address this, we propose CFQP—a collaborative filtering–enhanced question prediction framework—that models personalized temporal behavioral patterns via a dedicated memory module and incorporates a graph neural network–based preference propagation mechanism to jointly leverage individual historical interactions and collaborative signals from behaviorally similar users. CFQP is the first approach to achieve organic integration of language modeling and sequential behavioral modeling, significantly improving both predictive accuracy and behavioral plausibility in next-question prediction. This advances the paradigm for proactive, personalized conversational systems. Extensive experiments on multiple real-world datasets demonstrate that CFQP consistently outperforms state-of-the-art baselines.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Question Answering
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Community question answering
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In recent years, large language models (LLMs) have excelled in language understanding and generation, powering advanced dialogue and recommendation systems. However, a significant limitation persists: these systems often model user preferences statically, failing to capture the dynamic and sequential nature of interactive behaviors. The sequence of a user's historical questions provides a rich, implicit signal of evolving interests and cognitive patterns, yet leveraging this temporal data for predictive tasks remains challenging due to the inherent disconnect between language modeling and behavioral sequence modeling. To bridge this gap, we propose a Collaborative Filtering-enhanced Question Prediction (CFQP) framework. CFQP dynamically models evolving user-question interactions by integrating personalized memory modules with graph-based preference propagation. This dual mechanism allows the system to adaptively learn from user-specific histories while refining predictions through collaborative signals from similar users. Experimental results demonstrate that our approach effectively generates agents that mimic real-user questioning patterns, highlighting its potential for building proactive and adaptive dialogue systems.

Problem

• Predicting next user questions using dynamic behavioral sequences
• Modeling evolving user interests through historical question patterns
• Bridging language modeling with collaborative behavioral sequence analysis

Innovation

• Integrating personalized memory modules for dynamic modeling
• Using graph-based preference propagation for collaborative signals
• Generating agents that mimic real-user questioning patterns