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：个性是表现人们喜好的心理因素，进而影响其决策。我们假设准确地建模用户的个性会提高推荐系统的性能。然而，获取这样的个性描述既敏感又昂贵。我们通过引入一种新的方法来自动提取公共产品评论文本中的个性描述来解决这个问题。然后设计和评估三个利用这些个性描述的上下文感知推荐体系结构来测试我们的假说。对我们贡献的两个新个性数据集Amazon-beauty和Amazon-music的实验验证了我们的假设，展示了3-28%的性能提升。我们的分析揭示了不同的个性类型在推荐性能上的不同贡献：开放和外向的个性在音乐推荐中最有帮助，而有责任感的个性在美容产品推荐中最有帮助。