dorsal/arxiv
View SchemaA Framework for Personalized Persuasiveness Prediction via Context-Aware User Profiling
| Authors | Sejun Park, Yoonah Park, Jongwon Lim, Yohan Jo |
|---|---|
| Categories | |
| ArXiv ID | 2601.05654vv2 |
| URL | https://arxiv.org/abs/2601.05654 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
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.
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"date_created": "2026-02-17T05:53:03.993000Z",
"date_modified": "2026-02-17T05:53:03.993000Z",
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"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\u0027s characteristics, such as their values, experiences, and reasoning styles, there is currently no established systematic framework to optimize leveraging a persuadee\u0027s 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\u0027s 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.",
"arxiv_id": "2601.05654",
"authors": [
"Sejun Park",
"Yoonah Park",
"Jongwon Lim",
"Yohan Jo"
],
"categories": [
"cs.CL",
"cs.AI"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "A Framework for Personalized Persuasiveness Prediction via Context-Aware User Profiling",
"url": "https://arxiv.org/abs/2601.05654",
"version": "v2"
},
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"variant": "snapshot-2026-01-17",
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