Automatic trailer detection in multimedia content

A system comprising:

• at least one physical processor;
• and
• physical memory comprising computer-executable instructions that, when executed by the physical processor, cause the physical processor to:
• access at least one media item;
• segment the accessed media item into a plurality of video shots;
• instantiate a trained machine learning model that has been trained to automatically identify video shots in media items according to a relative similarity score that indicates a similarity between matched and unmatched video shots;
• and
• apply the trained machine learning model to the media item to generate a recommendation score for the plurality of video shots, the recommendation score indicating a degree to which each video shot is recommended for inclusion in a media item trailer that corresponds to the media item, wherein generating the recommendation score includes maximizing a comparative contrast between matched video shots and unmatched video shots.

A non-transitory computer-readable medium comprising one or more computer-executable instructions that, when executed by at least one processor of a computing device, cause the computing device to:

• access at least one media item;
• segment the accessed media item into a plurality of video shots;
• instantiate a trained machine learning model that has been trained to automatically identify video shots in media items according to a relative similarity score that indicates a similarity between matched and unmatched video shots;
• and
• apply the trained machine learning model to the media item to generate a recommendation score for the plurality of video shots, the recommendation score indicating a degree to which each video shot is recommended for inclusion in a media item trailer that corresponds to the media item, wherein generating the recommendation score includes maximizing a comparative contrast between matched video shots and unmatched video shots.