Embodiments provide for parsing a media file using a factor of interest, determining a factor score for the media file, and performing a scored action based on the factor score to provide a media content recommendation to a user/consumer or to content providers. The scored action may include sorting and filtering a media repository, including the media file, which in turn reduces an amount of data needed for a system to provide an objective recommendation to a user, as well as reducing the time and data processing required to provide a recommendation to the user.
BACKGROUND
The entertainment industry is increasingly moving towards providing media content directly to individual consumers via streaming services and other direct to consumer methods. As a part of this transition, the amount of digital content available to consumers is also rapidly increasing. This digital content includes content created for mass market appeal, where the content is intended for consumption across large populations, such as an entire country or consumers around the world. Other digital content includes content created for more niche market subsets of consumers, where the content may be readily enjoyed by some consumers, but less enjoyed by others.
Entertainment companies and other media content creators increasingly own and generate large amounts of media content across many different genres and media formats. The general goal of these companies and creators is to provide the media content to consumers for media consumption (e.g., viewing, listening, reading, etc.). As the media content landscape grows and the amount of digital content increases, the owners, creators, producers, reviewers, etc. (herein stakeholders) of the media content desire to understand what content currently exists in various media libraries, what current media content consumers want to consume, and what media content should be created to match consumer expectations for the future
Content creators, providers, and consumers all desire for more efficient ways to bot
1. A method comprising: parsing a media file for a plurality of scoring elements using a factor of interest; determining a media distribution of one or more dimensions of the factor of interest for the media file based on a presence of the one or more dimensions of the factor of interest in the plurality of scoring elements; generating a reference distribution of the one or more dimensions of the factor of interest based on a presence of the one or more dimensions of the of the factor of interest in a reference dataset; determining a factor score for the factor of interest in the media file based on the media distribution and the reference distribution; generating a scored action for the media file using the factor score; and performing the scored action by at least filtering a media repository based on the scored action and the factor score. ||
8. A system, comprising: a processor; and a memory comprising instructions which, when executed on the processor, performs an operation, the operation comprising: parsing a media file for a plurality of scoring elements using a factor of interest; determining a media distribution of one or more dimensions of the factor of interest for the media file based on a presence of the one or more dimensions of the factor of interest in the plurality of scoring elements; generating a reference distribution of the one or more dimensions of the factor of interest based on a presence of the one or more dimensions of the factor of interest in a reference dataset; determining a factor score for the factor of interest in the media file based on the media distribution and the reference distribution; generating a scored action for the media file using the factor score; and performing the scored action by at least filtering a media repository based on the scored action and the factor score. ||
15. A computer-readable storage medium comprising computer-readable program code embodied therewith, the computer-readable program code is configured to perform, when executed by a processor, an operation, the operation comprising: parsing a media file for a plurality of scoring elements using a factor of interest; determining a media distribution of one or more dimensions of the factor of interest for the media file based on a presence of the one or more dimensions of the factor of interest in the plurality of scoring elements; generating a reference distribution of the one or more dimensions of the factor of interest based on a presence of the one or more dimensions of the of the factor of interest in a reference dataset; determining a factor score for the factor of interest in the media file based on the media distribution and the reference distribution; generating a scored action for the media file using the factor score; and performing the scored action by at least filtering a media repository based on the scored action and the factor score.