A Framework for Personalized Persuasiveness Prediction via Context-Aware User Profiling

2026-01-09

arXiv.org

AI Summary

This work addresses the lack of a systematic framework in existing approaches for leveraging users’ historical behavior to model personalized traits and enhance persuasiveness prediction. To this end, we propose a context-aware dynamic user profiling mechanism that, for the first time, introduces a task-oriented, context-dependent trainable query generator to retrieve persuasion-relevant records from user history and dynamically construct user representations through a dedicated profiling module. The proposed framework is seamlessly integrated into multiple persuasiveness prediction models and evaluated on the ChangeMyView dataset, achieving up to a 13.77 percentage point improvement in F1 score over current state-of-the-art methods. These results demonstrate the effectiveness of dynamic user profiling in significantly boosting both predictive performance and generalization capability.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

Estimating the persuasiveness of messages is critical in various applications, from recommender systems to safety assessment of LLMs. While it is imperative to consider the target persuadee's characteristics, such as their values, experiences, and reasoning styles, there is currently no established systematic framework to optimize leveraging a persuadee's past activities (e.g., conversations) to the benefit of a persuasiveness prediction model. To address this problem, we propose a context-aware user profiling framework with two trainable components: a query generator that generates optimal queries to retrieve persuasion-relevant records from a user's history, and a profiler that summarizes these records into a profile to effectively inform the persuasiveness prediction model. Our evaluation on the ChangeMyView Reddit dataset shows consistent improvements over existing methods across multiple predictor models, with gains of up to +13.77%p in F1 score. Further analysis shows that effective user profiles are context-dependent and predictor-specific, rather than relying on static attributes or surface-level similarity. Together, these results highlight the importance of task-oriented, context-dependent user profiling for personalized persuasiveness prediction.

Problem

• personalized persuasiveness prediction
• context-aware user profiling
• persuasion modeling
• user history utilization
• LLM safety assessment

Innovation

• context-aware user profiling
• personalized persuasiveness prediction
• query generation
• dynamic user modeling
• LLM safety assessment