  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.
