Most current media streaming services use features that are constrained within the activity space of their platform, including popularity ratings and interaction history, to determine media  recommendations for users. This project proposes the idea of a media recommendation system that leverages specific everyday happenings related to the user outside of the activity space of the platform to generate recommendations. In our work, we developed a prototype movie recommendation system and conducted a controlled study to understand the important dimensions to consider in the design of such a recommendation system. Our system analyzes the user’s social media posts as a way to access his/her everyday happenings. Our study then evaluated the degree of relevance between social media posts and movie recommendations. Findings indicate that important aspects of the design of a social media-based movie recommendation system include the emotional context and extracted keywords of the user’s social media posts along with the movie genre.