  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 to generate personalized recommendations
and item cold starting. In this talk, we will focus on one particular type of
metadata - \textit{genre} labels. Genre labels associated with a movie or a TV
series help categorize a collection of titles into different themes and
correspondingly setting up the audience expectation. We present some of the
challenges associated with using genre label information and propose a new way
of examining the genre information that we call as the \textit{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.
Furthermore, we also talk about applications of LLMs in augmenting content
metadata which could eventually be used to achieve effective organization of
recommendations in user's 2-D home-grid.
