Recommendation systems are an integral part of any business, and a crucial factor in determining their success. Conventional methods of building recommendation systems such as collaborative filtering and content-based recommendation, although effective, suffer from limitations such as cold start and the data sparsity problems. Moreover, these methods aim at finding similar products as user’s past interactions rather than personalizing the recommendations. The upsurge in use of social media, over-the-top content (OTT), and e-commerce platforms has made the task of personalizing recommendations imperative, leading to the advancement of an area of research called psychology aware recommendation systems. This paper first surveys the various ways in which these new types of systems incorporate concepts of psychology while making recommendations. We then propose a new way of incorporating personality information that centers around user behavior on a particular platform. Users showing similar behavior can be deemed to prefer similar content. Our experimental results show that by identifying the right behavioral information to include in recommendations gives a 21% performance gain over existing baseline systems.