在SAMI中融入人格因素的社会推荐：探索人格检测在匹配中的作用
Personality-Enhanced Social Recommendations in SAMI: Exploring the Role of Personality Detection in Matchmaking
AI总结：
本文提出利用GPT零样本能力推断大五人格特质，整合至SAMI社交推荐系统，探索人格检测对社交匹配的影响。
摘要：
社会归属是学习的重要组成部分，然而在线课程环境却阻碍了社交群体的自然形成。SAMI（社交代理中介互动）通过促进学生之间的连接提供了一种解决方案，但其效果可能受到不完整的理论思维限制，限制了其创建有效'心理模型'的能力。其中一方面就是其无法直观地推断人格，这可能影响其推荐的相关性。为了探索这一差距，我们通过提出一个利用GPT的零样本能力从论坛介绍帖子推断大五人格特质的人格检测模型，来检验自动化人格推断的可行性。我们将其性能与已有的模型进行对比，发现虽然GPT模型在特定数据集上表现出色，但不同特质的表现差异显著。我们发现对乐观特质推断存在偏见，尤其是对于分布倾斜的特质。我们展示了将人格检测整合到SAMI基于实体的匹配系统中的概念验证，重点在于三个与积极社交形成有已建立联系的特质：外向性、宜人性和开放性。这项工作代表了在教育环境中进行基于人格的社会推荐的初步探索。尽管我们的实现显示了技术可行性，但仍有许多问题有待解决。我们讨论了这些限制，并概述了未来工作的方向，探讨了LLMs在进行人格推断时具体捕捉了什么，以及基于人格的匹配是否在实践中能显著改善学生之间的联系。
英文摘要：
Social belonging is a vital part of learning, yet online course environments present barriers to the organic formation of social groups. SAMI (Social Agent Mediated Interactions) offers one solution by facilitating student connections, but its effectiveness may be constrained by an incomplete Theory of Mind, limiting its ability to create an effective 'mental model' of a student. One facet of this is its inability to intuit personality, which may influence the relevance of its recommendations. To explore this gap, we examine the viability of automated personality inference by proposing a personality detection model utilizing GPT's zeroshot capability to infer Big-Five personality traits from forum introduction posts, often encouraged in online courses. We benchmark its performance against established models, finding that while GPT models show promising results on this specific dataset, performance varies significantly across traits. We identify potential biases toward optimistic trait inference, particularly for traits with skewed distributions. We demonstrate a proof-of-concept integration of personality detection into SAMI's entity-based matchmaking system, focusing on three traits with established connections to positive social formation: Extroversion, Agreeableness, and Openness. This work represents an initial exploration of personality-informed social recommendations in educational settings. While our implementation shows technical feasibility, significant questions remain. We discuss these limitations and outline directions for future work, examining what LLMs specifically capture when performing personality inference and whether personality-based matching meaningfully improves student connections in practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
