Personality is a psychological factor that reflects people's preferences, which in turn influences their decision-making. We hypothesize that accurate modeling of users' personalities improves recommendation systems' performance. However, acquiring such personality profiles is both sensitive and expensive. We address this problem by introducing a novel method to automatically extract personality profiles from public product review text. We then design and assess three context-aware recommendation architectures that leverage the profiles to test our hypothesis. Experiments on our two newly contributed personality datasets -- Amazon-beauty and Amazon-music -- validate our hypothesis, showing performance boosts of 3--28%.Our analysis uncovers that varying personality types contribute differently to recommendation performance: open and extroverted personalities are most helpful in music recommendation, while a conscientious personality is most helpful in beauty product recommendation.

翻译：人格是反映人们偏好的心理因素，进而影响其决策。我们假设，对用户人格的精确建模能够提升推荐系统的性能。然而，获取此类人格画像既敏感又昂贵。针对这一问题，我们提出了一种从公开产品评论语料中自动提取人格画像的新方法。随后，我们设计并评估了三种利用该画像的上下文感知推荐架构，以验证假设。基于两个新贡献的人格数据集（Amazon-beauty 和 Amazon-music）的实验验证了我们的假设，性能提升达3%–28%。分析表明，不同类型的人格对推荐性能的贡献存在差异：开放型和外向型人格在音乐推荐中最具助益，而尽责型人格则在美容产品推荐中作用最显著。