Beyond Labels: Leveraging Deep Learning and LLMs for Content Metadata
This work addresses metadata limitations in movie recommender systems for improving personalization and cold-start recommendations, but it appears incremental as it builds on existing deep learning and LLM methods.
The paper tackles the challenge of using genre labels in movie recommender systems by introducing the Genre Spectrum to capture nuanced genres, with offline and online experiments showing its effectiveness. It also explores using LLMs to augment content metadata for better recommendation organization.
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.