用文本表格融合的动态模型，让用户画像更准更可解释。
ExBigBang: A Dynamic Approach for Explainable Persona Classification through Contextualized Hybrid Transformer Analysis
- 结合文本与表格数据，用Transformer动态建模用户上下文。
- 在基准数据集上准确率显著优于传统方法。
- 适合需要透明决策依据的产品设计与用户研究场景。
在以用户为中心的设计中，用户画像对理解行为、捕捉需求、细分受众和指导设计至关重要。然而，用户交互日益复杂，亟需更贴近真实情境的建模方式。现有方法虽能建模用户行为，但整合文本与表格数据以获取深层上下文仍具挑战，且普遍缺乏可解释性。为此，我们提出ExBigBang（可解释的BigBang），一种融合文本与表格的混合方法，利用基于Transformer的架构建模丰富的上下文特征进行用户画像分类。该模型引入元数据、领域知识与用户画像信息，增强预测的上下文深度。通过用户画像与分类的循环迭代，模型可动态适应用户行为变化。在基准用户画像数据集上的实验表明模型具有强鲁棒性；消融实验证明文本与表格数据融合的优势；可解释AI技术揭示了模型决策的关键依据。
原文摘要 · Abstract (English)
In user-centric design, persona development plays a vital role in understanding user behaviour, capturing needs, segmenting audiences, and guiding design decisions. However, the growing complexity of user interactions calls for a more contextualized approach to ensure designs align with real user needs. While earlier studies have advanced persona classification by modelling user behaviour, capturing contextual information, especially by integrating textual and tabular data, remains a key challenge. These models also often lack explainability, leaving their predictions difficult to interpret or justify. To address these limitations, we present ExBigBang (Explainable BigBang), a hybrid text-tabular approach that uses transformer-based architectures to model rich contextual features for persona classification. ExBigBang incorporates metadata, domain knowledge, and user profiling to embed deeper context into predictions. Through a cyclical process of user profiling and classification, our approach dynamically updates to reflect evolving user behaviours. Experiments on a benchmark persona classification dataset demonstrate the robustness of our model. An ablation study confirms the benefits of combining text and tabular data, while Explainable AI techniques shed light on the rationale behind the model's predictions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。