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Personality is a psychological factor that reflects people's preferences,which in turn influences their decision-making. We hypothesize that accuratemodeling 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 extractpersonality profiles from public product review text. We then design and assessthree context-aware recommendation architectures that leverage the profiles totest our hypothesis.  Experiments on our two newly contributed personality datasets --Amazon-beauty and Amazon-music -- validate our hypothesis, showing performanceboosts of 3--28%.Our analysis uncovers that varying personality typescontribute differently to recommendation performance: open and extrovertedpersonalities are most helpful in music recommendation, while a conscientiouspersonality is most helpful in beauty product recommendation.

Improving Recommendation Systems with User Personality Inferred from Product Reviews