Beyond Labels: Leveraging Deep Learning and LLMs for Content Metadata

TL;DR

This paper explores leveraging deep learning and large language models to enhance content metadata analysis, focusing on genre labels through the innovative 'Genre Spectrum' approach to improve movie recommendation systems.

Contribution

Introduces the 'Genre Spectrum' method to better capture nuanced genres and discusses how LLMs can augment metadata for improved recommendation organization.

Findings

Genre Spectrum effectively captures nuanced genre information.

Offline and online experiments validate the approach.

LLMs can augment metadata for better recommendation layouts.

Abstract

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.