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. The dataset is available at https://github.com/XinyuanLu00/IRS-WSDM2023-personality-dataset.

翻译：人格是反映人们偏好的心理因素，进而影响其决策。我们假设精准建模用户人格能够提升推荐系统的性能。然而，获取此类人格画像既涉及敏感信息又代价高昂。我们通过提出一种从公开产品评论文本中自动提取人格画像的新方法来解决该问题。随后，我们设计并评估了三种利用这些画像来检验假设的上下文感知推荐架构。在两个新贡献的人格数据集——Amazon-beauty和Amazon-music上的实验验证了我们的假设，性能提升达3–28%。我们的分析揭示，不同人格类型对推荐性能的贡献存在差异：开放性和外向性人格对音乐推荐的帮助最大，而尽责性人格对美妆产品推荐的帮助最大。数据集获取地址为https://github.com/XinyuanLu00/IRS-WSDM2023-personality-dataset。