Content metadata plays a very important role in movie recommender systems as it provides valuable information about various aspects of a movie such as genre, cast, plot synopsis, box office summary, etc. Analyzing the metadata can help understand the user preferences and generate personalized recommendations catering to the niche tastes of the users. It can also help with content cold starting when the recommender system has little or no interaction data available to perform collaborative filtering. In this talk, we will focus on one particular type of metadata – genre labels. Genre labels associated with a movie or a TV series such as “horror” or “comedy” or “romance” help categorize a collection of movies into different themes and correspondingly setting up the audience expectation for a title. We present some of the challenges associated with using genre label information via traditional methods and propose a new way of examining the genre information that we call as the Genre Spectrum. The Genre Spectrum helps capture the various nuanced genres in a title and our offline and online experiments corroborate the effectiveness of the approach.